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Journal

  • Neil T. Dantam and Mike Stilman The Motion Grammar: Analysis of a Linguistic Method for Robot Control IEEE/RAS Transactions on Robotics. no. 3. 2013.

    We present the Motion Grammar: an approach to represent and verify robot control policies based on Context-Free Grammars. The production rules of the grammar represent a top-down task decomposition of robot behavior. The terminal symbols of this language represent sensor readings that are parsed in real-time. Efficient algorithms for context-free parsing guarantee that online parsing is computationally tractable. We analyze verification properties and language constraints of this linguistic modeling approach, show a linguistic basis that unifies several existing methods, and demonstrate effectiveness through experiments on a 14-DOF manipulator interacting with 32 objects (chess pieces) and an unpredictable human adversary. We provide many of the algorithms discussed as Open Source, permissively licensed software.

    @article{dantam2013motion,
      title = {The Motion Grammar: Analysis of a Linguistic Method for Robot Control},
      number = {3},
      volume = {29},
      pages = {704--718},
      journal = {IEEE/RAS Transactions on Robotics},
      author = {Neil T. Dantam and Mike Stilman},
      year = {2013}
    }
    
  • Mike Stilman Global Manipulation Planing in Robot Joint Space with Task Constraints IEEE/RAS Transactions on Robotics. no. 3. 2010.

    We explore global randomized joint space path planning for articulated robots that are subject to task space constraints. This paper describes a representation of constrained motion for joint space planners and develops two simple and efficient methods for constrained sampling of joint configurations: Tangent Space Sampling (TS) and First-Order Retraction (FR). FR is formally proven to provide global sampling for linear task space transformations. Constrained joint space planning is important for many real world problems involving redundant manipulators. On the one hand, tasks are designated in work space coordinates: rotating doors about selected axes, sliding drawers along selected trajectories or holding objects level during transport. On the other, joint space planning gives alternative paths that use redundant degrees of freedom to avoid obstacles or satisfy additional goals while performing a task. We demonstrate that our methods are faster and more invariant to parameter choices than existing techniques

    @article{stilman2010global,
      title = {Global Manipulation Planing in Robot Joint Space with Task Constraints},
      number = {3},
      volume = {26},
      pages = {576--584},
      journal = {IEEE/RAS Transactions on Robotics},
      author = {Mike Stilman},
      year = {2010}
    }
    
  • Mike Stilman and James Kuffner Planning Among Movable Obstacles with Artificial Constraints International Journal of Robotics Research. no. 12. 2008.

    This paper presents artificial constraints as a method for guiding heuristic search in the computationally challenging domain of motion planning among movable obstacles. The robot is permitted to manipulate unspecified obstacles in order to create space for a path. A plan is an ordered sequence of paths for robot motion and object manipulation. We show that under monotone assumptions, anticipating future manipulation paths results in constraints on both the choice of objects and their placements at earlier stages in the plan. We present an algorithm that uses this observation to incrementally reduce the search space and quickly find solutions to previously unsolved classes of movable obstacle problems. Our planner is developed for arbitrary robot geometry and kinematics. It is presented with an implementation for the domain of navigation among movable obstacles

    @article{stilman2008planning,
      title = {Planning Among Movable Obstacles with Artificial Constraints},
      number = {12},
      volume = {27},
      pages = {1295--1307},
      journal = {International Journal of Robotics Research},
      author = {Mike Stilman and James Kuffner},
      year = {2008}
    }
    
  • Satoshi Kagami, Koichi Nishiwaki, James Kuffner, Simon Thompson, Joel Chestnutt, Mike Stilman, and Philipp Michel Humanoid HRP2-DHRC for Autonomous and Interactive Behavior Robotics Research. 2007.

    Recently, research on humanoid-type robots has become increasingly active, and a broad array of fundamental issues are under investigation. However, in order to achieve a humanoid robot which can operate in human environ- ments, not only the fundamental components themselves, but also the suc- cessful integration of these components will be required. At present, almost all humanoid robots that have been developed have been designed for bipedal locomotion experiments. In order to satisfy the functional demands of loco- motion as well as high-level behaviors, humanoid robots require good me- chanical design, hardware, and software which can support the integration of tactile sensing, visual perception, and motor control. Autonomous behaviors are currently still very primitive for humanoid-type robots. It is difficult to conduct research on high-level autonomy and intelligence in humanoids due to the development and maintenance costs of the hardware. We believe low- level autonomous functions will be required in order to conduct research on higher-level autonomous behaviors for humanoids.

    @article{kagami2007hrp2,
      title = {Humanoid HRP2-DHRC for Autonomous and Interactive Behavior},
      volume = {28},
      pages = {103--117},
      journal = {Robotics Research},
      author = {Satoshi Kagami and Nishiwaki, Koichi and James Kuffner and Thompson, Simon and Chestnutt, Joel and Mike Stilman and Michel, Philipp},
      year = {2007}
    }
    
  • Mike Stilman, Koichi Nishiwaki, Satoshi Kagami, and James Kuffner Planning and Executing Navigation Among Movable Obstacles Springer Journal of Advanced Robotics. no. 14. 2007.

    This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain of Navigation Among Movable Obstacles (NAMO). The robot perceives and constructs a model of an environment filled with various fixed and movable obstacles, and automatically plans a navigation strategy to reach a desired goal location. The planned strategy consists of a sequence of walking and compliant manipulation operations. It is executed by the robot with online feedback. We give an overview of our NAMO system, as well as provide details of the autonomous planning, online grasping and compliant hand positioning during dynamically-stable walking. Finally, we present results of a successful implementation running on the Humanoid Robot HRP-2.

    @article{stilman2007planning,
      title = {Planning and Executing Navigation Among Movable Obstacles},
      number = {14},
      volume = {21},
      pages = {1617--1634},
      journal = {Springer Journal of Advanced Robotics},
      author = {Mike Stilman and Nishiwaki, Koichi and Satoshi Kagami and James Kuffner},
      year = {2007}
    }
    
  • Mike Stilman and James Kuffner Navigation Among Movable Obstacles: Real-Time Reasoning in Complex Environments International Journal on Humanoid Robotics. no. 4. 2005.

    In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfgure the environment by moving obstacles and clearing free space for a path. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specified navigation task, rather than the dimensionality of the multiobject domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.

    @article{stilman2005navigation,
      title = {Navigation Among Movable Obstacles: Real-Time Reasoning in Complex Environments},
      number = {4},
      volume = {2},
      pages = {479--504},
      month = {December},
      journal = {International Journal on Humanoid Robotics},
      author = {Mike Stilman and James Kuffner},
      year = {2005}
    }
    

Books and Chapters

  • Mike Stilman Autonomous Manipulation of Movable Obstacles Ch. 8. Ed. Kensuke Harada, Eiichi Yoshida, and Kazuhito Yokoi. University of Chicago Press. 2010.

    In this chapter we describe recent progress towards autonomous manipulation of environment objects. Many tasks, such as nursing home assistance, construction or search and rescue, require the robot to not only avoid obstacles but also move them out if its way to make space for reaching the goal. We present algorithms that decide which objects should be moved, where to move them and how to move them. Finally, we introduce a complete system that takes into account humanoid balance, joint limits and fullbody constraints to accomplish environment interaction.

    @inbook{stilman2010mphr,
      title = {Autonomous Manipulation of Movable Obstacles},
      publisher = {University of Chicago Press},
      chapter = {8},
      edition = {13},
      editor = {Kensuke Harada and Eiichi Yoshida and Kazuhito Yokoi},
      author = {Mike Stilman},
      year = {2010}
    }
    

Conference

  • 2014
  • Martin Levihn, Koichi Nishiwaki, Satoshi Kagami, and Mike Stilman Autonomous Environment Manipulation to Assist Humanoid Locomotion IEEE International Conference on Robotics and Automation. 2014.

    Legged robots have unique capabilities to traverse complex environments by stepping over and onto objects. Many footstep planners have been developed to take advantage of these capabilities. However, legged robots also have inherent constraints such as a maximum step height and distance. These constraints typically limit their reachable space, independent of footstep planning. Thus, we propose that robots such as humanoid robots that have manipulation capabilities should use them. A robot should autonomously modify its environment if necessary. We present a system that enabled a real robot to use a box to create itself a stair step or place a board on the ground to cross a gap, allowing it to reach its otherwise unreachable goal configuration.

    @inproceedings{levihn2014ICRA,
      title = {Autonomous Environment Manipulation to Assist Humanoid Locomotion},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Martin Levihn and Nishiwaki, Koichi and Kagami, Satoshi and Mike Stilman},
      year = {2014}
    }
    
  • 2013
  • Martin Levihn, Leslie Pack Kaelbling, Tomas Lozano-Perez, and Mike Stilman Foresight and Reconsideration in Hierarchical Planning and Execution IEEE/RSJ International Conference on Intelligent Robots and Systems. 2013.

    Legged robots have unique capabilities to traverse complex environments by stepping over and onto objects. Many footstep planners have been developed to take advantage of these capabilities. However, legged robots also have inherent constraints such as a maximum step height and distance. These constraints typically limit their reachable space, independent of footstep planning. Thus, we propose that robots such as humanoid robots that have manipulation capabilities should use them. A robot should autonomously modify its environment if necessary. We present a system that enabled a real robot to use a box to create itself a stair step or place a board on the ground to cross a gap, allowing it to reach its otherwise unreachable goal configuration.

    @inproceedings{levihn2013IROS,
      title = {Foresight and Reconsideration in Hierarchical Planning and Execution},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Martin Levihn and Kaelbling, Leslie Pack and Lozano-Perez, Tomas and Mike Stilman},
      year = {2013}
    }
    
  • Jonathan Scholz, Martin Levihn, and Charles L. Isbell What Does Physics Bias: A Comparison of Model Priors for Robot Manipulation 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making. 2013.

    We explore robot object manipulation as a Bayesian model-based reinforcement learning problem under a collection of different model priors. Our main contribution is to highlight the limitations of classical non-parametric regression approaches in the context of online learning, and to introduce an alternative approach based on monolithic physical inference. The primary motivation for this line of research is to incorporate physical system identification into the RL model, where it can be integrated with modern approaches to Bayesian structure learning. Overall, our results support the idea that modern physical simulation tools provide a model space with an appropriate inductive bias for manipulation problems in natural environments.

    @inproceedings{scholz2013RLDM,
      title = {What Does Physics Bias: A Comparison of Model Priors for Robot Manipulation},
      booktitle = {1st Multidisciplinary Conference on Reinforcement Learning and Decision Making},
      author = {Jonathan Scholz and Martin Levihn and Isbell, Charles L.},
      year = {2013}
    }
    
  • Neil T. Dantam, Ayonga Hereid, Aaron Ames, and Mike Stilman Correct Software Synthesis for Stable Speed-Controlled Robotic Walking Robotics: Science and Systems. 2013.

    We present a software synthesis method for speed-controlled robot walking based on supervisory control of a context-free Motion Grammar. First, we use Human-Inspired control to identify parameters for stable fixed speed walking and for transitions between fixed speeds. Next, we build a Motion Grammar representing the discrete-time control for this set of speeds. Then, we synthesize C code from this grammar and generate supervisors online to achieve desired walking speeds, ensuring correctness of discrete behavior. Finally, we demonstrate this approach on the Aldebaran NAO, showing stable walking transitions with dynamically selected speeds.

    @inproceedings{dantam2013rss,
      title = {Correct Software Synthesis for Stable Speed-Controlled Robotic Walking},
      month = {June},
      booktitle = {Robotics: Science and Systems},
      author = {Neil T. Dantam and Hereid, Ayonga and Ames, Aaron and Mike Stilman},
      year = {2013}
    }
    
  • Martin Levihn, Matthew Dutton, Alexander Trevor, and Mike Stilman Detecting Partially Occluded Objects via Segmentation and Validation IEEE Workshop on Robot Vision (WoRV). 2013.

    This paper presents a novel algorithm: Verfied Partial Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds. VPOD is implemented and validated on real sensor data obtained by our robot. It extends Viewpoint Feature Histograms (VFH) which classify unoccluded objects to also classifying partially occluded objects such as furniture that might be seen in typical office environments. To achieve this result, VPOD employs two strategies. First, object models are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the complete object model is aligned back into the scene and verified for consistency with the point cloud data. Overall, our approach increases the number of objects found and substantially reduces false positives due to the verification process.

    @inproceedings{levihn2013worv,
      title = {Detecting Partially Occluded Objects via Segmentation and Validation},
      booktitle = {IEEE Workshop on Robot Vision (WoRV)},
      author = {Martin Levihn and Dutton, Matthew and Trevor, Alexander and Mike Stilman},
      year = {2013}
    }
    
  • Can Erdogan and Mike Stilman Planning in Constraint Space: Automated Design of Functional Structures IEEE/RSJ International Conference on Robotics and Automation. 2013.

    On the path to full autonomy, robotic agents have to learn how to manipulate their environments for their benefit. In particular, the ability to design structures that are functional in overcoming challenges is imperative. The problem of automated design of functional structures (ADFS) addresses the question of whether the objects in the environment can be placed in a useful configuration. In this work, we first make the observation that the ADFS problem represents a class of problems in high dimensional, continuous spaces that can be broken down into simpler subproblems with semantically meaningful actions. Next, we propose a framework where discrete actions that induce constraints can partition the solution space effectively. Subsequently, we solve the original class of problems by searching over the available actions, where the evaluation criteria for the search is the feasibility test of the accumulated constraints. We prove that with a sound feasibility test, our algorithm is complete. Additionally, we argue that a convexity requirement on the constraints leads to significant efficiency gains. Finally, we present successful results to the ADFS problem.

    @inproceedings{erdogan2013ADFS,
      title = {Planning in Constraint Space: Automated Design of Functional Structures},
      pages = {1799-1804},
      month = {May},
      booktitle = {IEEE/RSJ International Conference on Robotics and Automation},
      author = {Can Erdogan and Mike Stilman},
      year = {2013}
    }
    
  • Kyel Ok, Sameer Ansari, Billy Gallagher, William Sica, Frank Dellaert, and Mike Stilman Path Planning with Uncertainty: Voronoi Uncertainty Fields IEEE/RSJ International Conference on Robotics and Automation. 2013.

    In this paper, a two-level path planning algorithm that deals with map uncertainty is proposed. The higher level planner uses modi?ed generalized Voronoi diagrams to guarantee ?nding a connected path from the start to the goal if a collision-free path exists. The lower level planner considers uncertainty of the observed obstacles in the environment and assigns repulsive forces based on their distance to the robot and their positional uncertainty. The attractive forces from the Voronoi nodes and the repulsive forces from the uncertaintybiased potential fields form a hybrid planner we call Voronoi Uncertainty Fields (VUF). The proposed planner has two strong properties: (1) bias against uncertain obstacles, and (2) completeness. We analytically prove the properties and run simulations to validate our method in a forest-like environment.

    @inproceedings{ok2013voronoi,
      title = {Path Planning with Uncertainty: Voronoi Uncertainty Fields},
      pages = {4581-4586},
      month = {May},
      booktitle = {IEEE/RSJ International Conference on Robotics and Automation},
      author = {Ok, Kyel and Ansari, Sameer and Gallagher, Billy and Sica, William and Dellaert, Frank and Mike Stilman},
      year = {2013}
    }
    
  • Martin Levihn, Jonathan Scholz, and Mike Stilman Planning with Movable Obstacles in Continuous Environments with Uncertain Dynamics IEEE International Conference on Robotics and Automation. 2013.

    In this paper we present a decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO) operating under conditions faced by real robotic systems. While planners for the NAMO domain exist, they typically assume a deterministic environment or rely on discretization of the configuration and action spaces, preventing their use in practice. In contrast, we propose a planner that operates in real-world conditions such as uncertainty about the parameters of workspace objects and continuous configuration and action (control) spaces. To achieve robust NAMO planning despite these conditions, we introduce a novel integration of Monte Carlo simulation with an abstract MDP construction. We present theoretical and empirical arguments for time complexity linear in the number of obstacles as well as a detailed implementation and examples from a dynamic simulation environment.

    @inproceedings{levihn2013MDP,
      title = {Planning with Movable Obstacles in Continuous Environments with Uncertain Dynamics},
      pages = {3817-3823},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Martin Levihn and Jonathan Scholz and Mike Stilman},
      year = {2013}
    }
    
  • Rowland O'Flaherty, Pete Vieira, M.X. Grey, Paul Oh, Aaron Bobick, Magnus Egerstedt, and Mike Stilman Humanoid Robot Teleoperation for Tasks with Power Tools IEEE International Conference on Technologies for Practical Robot Applications. 2013.

    This paper presents the implementation of inverse kinematics to achieve teleoperation of a physical humanoid robot platform. The humanoid platform will be used to compete in the DARPA Robot Challenge, which requires autonomous execution of various search and rescue tasks, such as cutting through walls, which is a very practical application to robotics. Using a closed-form kinematic solution and a basic feedback controller, our objective of executing simple tasks is realized via teleoperation. Joint limits and singularities are accounted for using the different cases in the kinematic solution; and a decision method is implemented to determine how to position the end-effector when the goal is outside the feasible workspace.

    @inproceedings{oflaherty2013teleop,
      title = {Humanoid Robot Teleoperation for Tasks with Power Tools},
      pages = {119-124},
      booktitle = {IEEE International Conference on Technologies for Practical Robot Applications},
      author = {Rowland O'Flaherty and Pete Vieira and M.X. Grey and Oh, Paul and Bobick, Aaron and Magnus Egerstedt and Mike Stilman},
      year = {2013}
    }
    
  • M.X. Grey, Neil T. Dantam, Dan M. Lofaro, Paul Oh, Aaron Bobick, Magnus Egerstedt, and Mike Stilman Multi-Process Control Software for Humanoid Robots IEEE International Conference on Technologies for Practical Robot Applications. 2013.

    Humanoid robots require greater software reliability than traditional mechantronic systems if they are to perform useful tasks in typical human-oriented environments. This paper covers a software architecture which distributes the load of computation and control tasks over multiple processes, enabling fail-safes within the software. These fail-safes ensure that unexpected crashes or latency do not produce damaging behavior in the robot. The distribution also offers benefits for future software development by making the architecture modular and extensible. Utilizing a low-latency inter-process communication protocol (Ach), processes are able to communicate with high control frequencies. The key motivation of this software architecture is to provide a practical framework for safe and reliable humanoid robot software development. The authors test and verify this framework on a HUBO2 Plus humanoid robot.

    @inproceedings{grey2013architecture,
      title = {Multi-Process Control Software for Humanoid Robots},
      pages = {190-195},
      booktitle = {IEEE International Conference on Technologies for Practical Robot Applications},
      author = {M.X. Grey and Neil T. Dantam and Lofaro, Dan M. and Oh, Paul and Bobick, Aaron and Magnus Egerstedt and Mike Stilman},
      year = {2013}
    }
    
  • 2012
  • Ana Huamán Quispe and Mike Stilman Deterministic Motion Planning for Redundant Robots along End-Effector Paths International Conference on Humanoid Robots (Humanoids). 2012.

    In this paper we propose a deterministic approach to solve the Motion Planning along End-Effector Paths problem (MPEP) for redundant manipulators. Most of the existing approaches are based on local optimization techniques, hence they do not offer global guarantees of finding a path if it exists. Our proposed method is resolution complete. This feature is achieved by discretizing the Jacobian nullspace at each waypoint and selecting the next configuration according to a given heuristic function. To escape from possible local minima, our algorithm implements a backtracking strategy that allows our planner to recover from erroneous previous configuration choices by performing a breadth-first backwards search procedure. We present the results of simulated experiments performed with diverse manipulators and a humanoid robot.

    @inproceedings{huaman2012nns,
      title = {Deterministic Motion Planning for Redundant Robots along End-Effector Paths},
      booktitle = {International Conference on Humanoid Robots (Humanoids)},
      author = {Ana Huamán Quispe and Mike Stilman},
      year = {2012}
    }
    
  • Neil T. Dantam and Mike Stilman Robust and Efficient Communication for Real-Time Multi-Process Robot Software International Conference on Humanoid Robots (Humanoids). 2012.

    We present a new Interprocess Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating drivers, controllers, and algorithms in complex robotic systems such as humanoid robots. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems, and we discuss the implementation on our humanoid robot Golem Krang. Finally, the source code for Ach is available under an Open Source permissive license.

    @inproceedings{dantam2012robust,
      title = {Robust and Efficient Communication for Real-Time Multi-Process Robot Software},
      booktitle = {International Conference on Humanoid Robots (Humanoids)},
      author = {Neil T. Dantam and Mike Stilman},
      year = {2012}
    }
    
  • Neil T. Dantam, Irfan Essa, and Mike Stilman Linguistic Transfer of Human Assembly Tasks to Robots IEEE/RSJ International Conference on Intelligent Robots and Systems. 2012.

    We demonstrate the automatic transfer of an assembly task from human to robot. This work extends efforts showing the utility of linguistic models in verifiable robot control policies by now performing real visual analysis of human demonstrations to automatically extract a policy for the task. This method tokenizes each human demonstration into a sequence of object connection symbols, then transforms the set of sequences from all demonstrations into an automaton, which represents the task-language for assembling a desired object. Finally, we combine this assembly automaton with a kinematic model of a robot arm to reproduce the demonstrated task.

    @inproceedings{dantam2012mgassem,
      title = {Linguistic Transfer of Human Assembly Tasks to Robots},
      pages = {237--242},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Neil T. Dantam and Irfan Essa and Mike Stilman},
      year = {2012}
    }
    
  • Tobias Kunz and Mike Stilman Manipulation Planning with Soft Task Constraints IEEE/RSJ International Conference on Intelligent Robots and Systems. 2012.

    We present a randomized configuration space planner that enforces soft workspace task constraints. A soft task constraint allows an interval of feasible values while favoring a given exact value. Previous work only allows for enforcing an exact value or an interval without a specific preference. Soft task constraints are a useful concept in everyday life. For example when carrying a container of liquid we want to keep it as close to the upright position as possible but want to be able to tilt it slightly in order to avoid obstacles. This paper introduces the necessary algorithms for handling such constraints, including projection methods and useful representations of everyday constraints. Our algorithms are evaluated on a series of simulated benchmark problems and shown to yield significant improvement in constraint satisfaction.

    @inproceedings{kunz2012soft,
      title = {Manipulation Planning with Soft Task Constraints},
      pages = {1937--1942},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Tobias Kunz and Mike Stilman},
      year = {2012}
    }
    
  • Neil T. Dantam and Mike Stilman The Motion Grammar Calculus for Context-Free Hybrid Systems American Control Conference. 2012. Best Presentation in Session

    This paper provides a method for deriving provably correct controllers for Hybrid Dynamical Systems with Context-Free discrete dynamics, nonlinear continuous dynamics, and nonlinear state partitioning. The proposed method models the system using a Context-Free Motion Grammar and specifies correct performance using a Regular language representation such as Linear Temporal Logic. The initial model is progressively rewritten via a calculus of symbolic transformation rules until it satisfies the desired specification.

    @inproceedings{dantam2012mgcalc,
      title = {The Motion Grammar Calculus for Context-Free Hybrid Systems},
      pages = {5294--5301},
      month = {June},
      booktitle = {American Control Conference},
      author = {Neil T. Dantam and Mike Stilman},
      year = {2012}
    }
    
  • Neil T. Dantam, Carlos Nieto-Granda, Henrik Christensen, and Mike Stilman Linguistic Composition of Semantic Maps and Hybrid Controllers International Symposium on Experimental Robotics. 2012.

    This work combines semantic maps with hybrid control models, generating a direct link between action and environment models to produce a control policy for mobile manipulation in unstructured environments. First, we generate a semantic map for our environment and design a base model of robot action. Then, we combine this map and action model using the Motion Grammar Calculus to produce a combined robot-environment model. Using this combined model, we apply supervisory control to produce a policy for the manipulation task. We demonstrate this approach on a Segway RMP-200 mobile platform.

    @inproceedings{dantam2012composition,
      title = {Linguistic Composition of Semantic Maps and Hybrid Controllers},
      pages = {17--21},
      month = {June},
      booktitle = {International Symposium on Experimental Robotics},
      author = {Neil T. Dantam and Carlos Nieto-Granda and Henrik Christensen and Mike Stilman},
      year = {2012}
    }
    
  • Scott Koziol, P. Hasler, and Mike Stilman Robot Path Planning Using Field Programmable Analog Arrays IEEE International Conference on Robotics and Automation. 2012.

    We present the successful application of reconfgurable Analog-Very-Large-Scale-Integrated(AVLSI) circuits to motion planning for the AmigoBot robot. Previous research has shown that custom application-specifc-integrated-circuits (ASICs) can be used for robot path planning. However, ASICs are typically fixed circuit designs that require long fabrication times on the order of months. In contrast, our reconfgurable analog circuits called Field Programmable Analog Arrays (FPAAs) implement a variety of AVLSI circuits in minutes. We present experimental results of online robot path planning using FPAA circuitry, validating our assertion that FPAA-based AVLSI design is a feasible approach to computing complete motion plans using analog foating-gate resistive grids. We demonstrate the integration of FPAA hardware and software with a real robot platform and hardware in the loop simulations, present the trajectories developed by our planner and provide analysis of the time and space complexity of our proposed approach. The paper concludes by formulating metrics that identify domains where analog solutions to planning may be faster and more effcient than traditional, digital robot planning techniques.

    @inproceedings{koziol2012robot,
      title = {Robot Path Planning Using Field Programmable Analog Arrays},
      pages = {1747--1752},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Koziol, Scott and P. Hasler and Mike Stilman},
      year = {2012}
    }
    
  • Tobias Kunz and Mike Stilman Time-Optimal Trajectory Generation for Path Following with Bounded Acceleration and Velocity Robotics: Science and Systems. 2012.

    This paper presents a novel method to generate the time-optimal trajectory that exactly follows a given differentiable joint-space path within given bounds on joint accelerations and velocities. We also present a path preprocessing method to make nondifferentiable paths differentiable by adding circular blends. We introduce improvements to existing work that make the algorithm more robust in the presence of numerical inaccuracies. Furthermore we validate our methods on hundreds of randomly generated test cases on simulated and real 7-DOF robot arms. Finally, we provide open source software that implements our algorithms.

    @inproceedings{kunz2012time,
      title = {Time-Optimal Trajectory Generation for Path Following with Bounded Acceleration and Velocity},
      pages = {09--13},
      month = {July},
      booktitle = {Robotics: Science and Systems},
      author = {Tobias Kunz and Mike Stilman},
      year = {2012}
    }
    
  • Martin Levihn, Takeo Igarashi, and Mike Stilman Multi-Robot Multi-Object Rearrangement in Assignment Space IEEE/RSJ International Conference on Intelligent Robots and Systems. 2012.

    We present Assignment Space Planning, a new effi- cient robot multi-agent coordination algorithm for the PSPACE- hard problem of multi-robot multi-object push rearrangement. In both simulated and real robot experiments, we demonstrate that our method produces optimal solutions for simple problems and exhibits novel emergent behaviors for complex scenarios. Assignment Space takes advantage of the domain structure by splitting the planning up into three stages, effectively reducing the search space size and enabling the planner to produce optimized plans in seconds. Our algorithm finds solutions of comparable quality to complete configuration space search while reducing the computing time to seconds, which allows our approach to be applied in practical scenarios in real-time.

    @inproceedings{levihn2012multi,
      title = {Multi-Robot Multi-Object Rearrangement in Assignment Space},
      pages = {5255--5261},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Martin Levihn and Takeo Igarashi and Mike Stilman},
      year = {2012}
    }
    
  • Martin Levihn, Jonathan Scholz, and Mike Stilman Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles Workshop on the Algorithmic Foundations of Robotics. 2012.

    In this paper we present the first decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO). While efficient planners for NAMO exist, they are challenging to implement in practice due to the inherent uncertainty in both perception and control of real robots. Generalizing existing NAMO planners to nondeterministic domains is particularly difficult due to the sensitivity of MDP methods to task dimensionality. Our work addresses this challenge by combining ideas from Hierarchical Reinforcement Learning with Monte Carlo Tree Search, and results in an algorithm that can be used for fast online planning in uncertain environments. We evaluate our algorithm in simulation, and provide a theoretical argument for our results which suggest linear time complexity in the number of obstacles for typical environments.

    @inproceedings{levihn2012hierarchical,
      title = {Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles},
      pages = {19--35},
      month = {June},
      booktitle = {Workshop on the Algorithmic Foundations of Robotics},
      author = {Martin Levihn and Jonathan Scholz and Mike Stilman},
      year = {2012}
    }
    
  • Jiuguang Wang, Eric Whitman, and Mike Stilman Whole-Body Trajectory Optimization for Humanoid Falling American Control Conference. 2012. Best Presentation in Session

    We present an optimization-based control strategy for generating whole-body trajectories for humanoid robots in order to minimize damage in falling. In this work, the falling problem is formulated using optimal control where we seek to minimize the impulse on impact with the ground, subject to the full-body dynamics and constraints of the robot in joint space. We extend previous work in this domain by numerically solving the resulting optimal control problem, generating open-loop trajectories by solving an equivalent nonlinear programming (NLP) problem. These results are illustrated in simulation using the models of dynamically balancing humanoid robots in both wheeled and legged forms. Through the comparison of falling trajectories for the two systems, we demonstrate the advantages of the proposed strategy over previous work in this domain for the effective reduction of the impulse at impact.

    @inproceedings{wang2012wholebody,
      title = {Whole-Body Trajectory Optimization for Humanoid Falling},
      pages = {4837--4842},
      month = {June},
      booktitle = {American Control Conference},
      author = {Jiuguang Wang and Whitman, Eric and Mike Stilman},
      year = {2012}
    }
    
  • 2011
  • Baris Akgun and Mike Stilman Sampling Heuristics for Optimal Motion Planning in High Dimension IEEE/RSJ International Conference on Intelligent Robots and Systems. 2011.

    We present a sampling-based motion planner that improves the performance of the probabilistically optimal RRT* planning algorithm. Experiments demonstrate that our planner finds a fast initial path and decreases the cost of this path iteratively. We identify and address the limitations of RRT* in high-dimensional configuration spaces. We introduce a sampling bias to facilitate and accelerate cost decrease in these spaces and a simple node-rejection criteria to increase efficiency. Finally, we incorporate an existing bi-directional approach to search which decreases the time to find an initial path. We analyze our planner on a simple 2D navigation problem in detail to show its properties and test it on a difficult 7D manipulation problem to show its effectiveness. Our results consistently demonstrate improved performance over RRT*

    @inproceedings{akgun2011sampling,
      title = {Sampling Heuristics for Optimal Motion Planning in High Dimension},
      pages = {2640--2645},
      month = {September},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Baris Akgun and Mike Stilman},
      year = {2011}
    }
    
  • Akansel Cosgun, Tucker Hermans, Victor Emeli, and Mike Stilman Push Planning for Object Placement on Cluttered Table Surfaces IEEE/RSJ International Conference on Intelligent Robots and Systems. 2011.

    We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation

    @inproceedings{cosgun2011push,
      title = {Push Planning for Object Placement on Cluttered Table Surfaces},
      pages = {4627--4632},
      month = {September},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Cosgun, Akansel and Tucker Hermans and Emeli, Victor and Mike Stilman},
      year = {2011}
    }
    
  • Neil T. Dantam, Pushkar Kolhe, and Mike Stilman The Motion Grammar for Physical Human-Robot Games IEEE International Conference on Robotics and Automation. 2011. SAIC/Georgia Tech Achievement Award

    We introduce the Motion Grammar, a powerful new representation for robot decision making, and validate its properties through the successful implementation of a physical human-robot game. The Motion Grammar is a formal tool for task decomposition and hybrid control in the presence of significant online uncertainty. In this paper, we describe the Motion Grammar, introduce some of the formal guarantees it can provide, and represent the entire game of human-robot chess through a single formal language. This language includes game-play, safe handling of human motion, uncertainty in piece positions, misplaced and collapsed pieces. We demonstrate the simple and effective language formulation through experiments on a 14-DOF manipulator interacting with 32 objects (chess pieces) and an unpredictable human adversary.

    @inproceedings{dantam2011chess,
      title = {The Motion Grammar for Physical Human-Robot Games},
      pages = {5463--5469},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Neil T. Dantam and Pushkar Kolhe and Mike Stilman},
      year = {2011}
    }
    
  • Neil T. Dantam and Mike Stilman The Motion Grammar: Linguistic Perception, Planning, and Control Robotics: Science and Systems. 2011.

    We present and analyze the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and control that provides both fast online control of robots in uncertain environments and the ability to guarantee completeness and correctness. The grammar represents a policy for the task which is parsed in real-time based on perceptual input. Branches of the syntax tree form the levels of a hierarchical decomposition, and the individual robot sensor readings are given by tokens. We implement this approach in the interactive game of Yamakuzushi on a physical robot resulting in a system that repeatably competes with a human opponent in sustained gameplay for the roughly six minute duration of each match.

    @inproceedings{dantam2011yama,
      title = {The Motion Grammar: Linguistic Perception, Planning, and Control},
      pages = {49--56},
      month = {June},
      booktitle = {Robotics: Science and Systems},
      author = {Neil T. Dantam and Mike Stilman},
      year = {2011}
    }
    
  • Tobias Kunz, Peter Kingston, Mike Stilman, and Magnus Egerstedt Dynamic Chess: Strategic Planning for Robot Motion IEEE International Conference on Robotics and Automation. 2011.

    We introduce and experimentally validate a novel algorithmic model for physical human-robot interaction with hybrid dynamics. Our computational solutions are complementary to passive and compliant hardware. We focus on the case where human motion can be predicted. In these cases, the robot can select optimal motions in response to human actions and maximize safety. By representing the domain as a Markov Game, we enable the robot to not only react to the human but also to construct an infinite horizon optimal policy of actions and responses. Experimentally, we apply our model to simulated robot sword defense. Our approach enables a simulated 7-DOF robot arm to block known attacks in any sequence. We generate optimized blocks and apply game theoretic tools to choose the best action for the defender in the presence of an intelligent adversary.

    @inproceedings{kunz2011dynamic,
      title = {Dynamic Chess: Strategic Planning for Robot Motion},
      pages = {3796--3803},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Tobias Kunz and Kingston, Peter and Mike Stilman and Magnus Egerstedt},
      year = {2011}
    }
    
  • 2010
  • Takeo Igarashi and Mike Stilman Homotopic Path Planning on Manifolds for Cabled Mobile Robots Workshop on the Algorithmic Foundations of Robotics. 2010.

    We present two path planning algorithms for mobile robots that are connected by cable to a fixed base. Our algorithms efficiently compute the shortest path and control strategy that lead the robot to the target location considering cable length and obstacle interactions. First, we focus on cable-obstacle collisions. We introduce and formally analyze algorithms that build and search an overlapped configuration space manifold. Next, we present an extension that considers cable-robot collisions. All algorithms are experimentally validated using a real robot.

    @inproceedings{Igarashi2010homotopic,
      title = {Homotopic Path Planning on Manifolds for Cabled Mobile Robots},
      pages = {1--18},
      month = {December},
      booktitle = {Workshop on the Algorithmic Foundations of Robotics},
      author = {Takeo Igarashi and Mike Stilman},
      year = {2010}
    }
    
  • Pushkar Kolhe, Neil T. Dantam, and Mike Stilman Dynamic Pushing Strategies for Dynamically Stable Mobile Manipulators IEEE International Conference on Robotics and Automation. 2010.

    This paper presents three effective manipulation strategies for wheeled, dynamically balancing robots with articulated links. By comparing these strategies through analysis, simulation and robot experiments, we show that contact placement and body posture have a significant impact on the robot's ability to accelerate and displace environment objects. Given object geometry and friction parameters we determine the most effective methods for utilizing wheel torque to perform non-prehensile manipulation.

    @inproceedings{kolhe2010dynamic,
      title = {Dynamic Pushing Strategies for Dynamically Stable Mobile Manipulators},
      pages = {3745--3750},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Pushkar Kolhe and Neil T. Dantam and Mike Stilman},
      year = {2010}
    }
    
  • Tobias Kunz, Ulrich Reiser, Mike Stilman, and Alexander Verl Real-Time Path Planning for a Robot Arm in Changing Environments IEEE/RSJ International Conference on Intelligent Robots and Systems. 2010.

    We present a practical strategy for real-time path planning for articulated robot arms in changing environments by integrating PRM for Changing Environments with 3D sensor data. Our implementation on Care-O-Bot 3 identifies bottlenecks in the algorithm and introduces new methods that solve the overall task of detecting obstacles and planning a path around them in under 100 ms. A fast planner is necessary to enable the robot to react to quickly changing human environments. We have tested our implementation in real-world experiments where a human subject enters the manipulation area, is detected and safely avoided by the robot. This capability is critical for future applications in automation and service robotics where humans will work closely with robots to jointly perform tasks

    @inproceedings{kunz2010real,
      title = {Real-Time Path Planning for a Robot Arm in Changing Environments},
      pages = {5906--5911},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Tobias Kunz and Reiser, Ulrich and Mike Stilman and Verl, Alexander},
      year = {2010}
    }
    
  • Jonathan Scholz and Mike Stilman Combining Motion Planning and Optimization for Flexible Robot Manipulation IEEE/RAS International Conference on Humanoid Robotics. 2010. Best Paper Award

    Robots that operate in natural human environments must be capable of handling uncertain dynamics and underspecified goals. Current solutions for robot motion planning are split between graph-search methods, such as RRT and PRM which offer solutions to high-dimensional problems, and Reinforcement Learning methods, which relieve the need to specify explicit goals and action dynamics. This paper addresses the gap between these methods by presenting a task-space probabilistic planner which solves general manipulation tasks posed as optimization criteria. Our approach is validated in simulation and on a 7-DOF robot arm that executes several tabletop manipulation tasks. First, this paper formalizes the problem of planning in underspecified domains. It then describes the algorithms necessary for applying this approach to planar manipulation tasks. Finally it validates the algorithms on a series of sample tasks that have distinct objectives, multiple objects with different shapes/dynamics, and even obstacles that interfere with object motion.

    @inproceedings{scholz2010combining,
      title = {Combining Motion Planning and Optimization for Flexible Robot Manipulation},
      pages = {80--85},
      month = {December},
      booktitle = {IEEE/RAS International Conference on Humanoid Robotics},
      author = {Jonathan Scholz and Mike Stilman},
      year = {2010}
    }
    
  • Martin Schuster, Richard Bormann, Daniela Steidl, Saul Reynolds-Haertle, and Mike Stilman Stable Stacking for the Distributor's Pallet Packing Problem IEEE/RSJ International Conference on Intelligent Robots and Systems. 2010.

    We present a novel algorithm that solves the distributor's pallet packing problem. In contrast to existing algorithms, our method optimizes stack stability in addition to stack volume. Furthermore, our algorithm explicitly handles cases where the construction of homogeneous layers of packages with equal height is impossible due to differences in package heights and quantities. The algorithm is a nested beam search that separately optimizes local and global evaluation criteria. We show successful results on both real world and synthetic data sets, compare our performance to an existing algorithm and demonstrate experimental applications in simulation and on a real palletizing robo

    @inproceedings{schuster2010stable,
      title = {Stable Stacking for the Distributor's Pallet Packing Problem},
      pages = {3646--3651},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Schuster, Martin and Bormann, Richard and Steidl, Daniela and Saul Reynolds-Haertle and Mike Stilman},
      year = {2010}
    }
    
  • Mike Stilman, Jon Olson, and William Gloss Golem Krang: Dynamically Stable Humanoid Robot for Mobile Manipulation IEEE International Conference on Robotics and Automation. 2010.

    What would humans be like if nature had invented the wheel? Golem Krang is a novel humanoid torso designed at Georgia Tech. The robot dynamically transforms from a .5 m static to a 1.5 m dynamic configuration. Our robot development has led to two advances in the design of platforms for mobility and manipulation: (1) A 2-DOF robot base that autonomously stands from horizontal rest; (2) A 4-DOF humanoid torso that adds a waist roll joint to replicate human torso folding and a yaw joint for spine rotation. The mobile torso also achieves autonomous standing in a constrained space while lifting a 40 kg payload. Golem validates our assertions by consistently achieving static-dynamic transformations. This paper describes the design of our mobile torso. It considers a number of factors including its suitability for human environments, mechanical simplicity and the ability to store potential and kinetic energy for handling heavy human and even super-human tasks

    @inproceedings{stilman2010golem,
      title = {Golem Krang: Dynamically Stable Humanoid Robot for Mobile Manipulation},
      pages = {3304--3309},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Mike Stilman and Olson, Jon and Gloss, William},
      year = {2010}
    }
    
  • Hai-Ning Wu, Martin Levihn, and Mike Stilman Navigation Among Movable Obstacles in Unknown Environments IEEE/RSJ International Conference on Intelligent Robots and Systems. 2010.

    This paper explores the Navigation Among Movable Obstacles (NAMO) problem in an unknown environment. We consider the realistic scenario in which the robot has to navigate to a goal position in an unknown environment consisting of static and movable objects. The robot may move objects if the goal can not be reached otherwise or if moving the object may significantly shorten the path to the goal. We consider real situations in which the robot only has limited sensing information and where the action selection can therefore only be based on partial knowledge learned from the environment at that point. This paper introduces an algorithm that significantly reduces the necessary calculations to accomplish this task compared to a direct approach. We present an efficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios with more than 50 objects

    @inproceedings{wu2010navigation,
      title = {Navigation Among Movable Obstacles in Unknown Environments},
      pages = {1433--1438},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Wu, Hai-Ning and Martin Levihn and Mike Stilman},
      year = {2010}
    }
    
  • Kasemsit Teeyapan, Jiuguang Wang, Tobias Kunz, and Mike Stilman Robot Limbo: Optimized Planning and Control for Dynamically Stable Robots Under Vertical Obstacles IEEE International Conference on Robotics and Automation. 2010.

    We present successful control strategies for dynamically stable robots that avoid low ceilings and other vertical obstacles in a manner similar to limbo dances. Given the parameters of the mission, including the goal and obstacle dimensions, our method uses a sequential composition of IOlinearized controllers and applies stochastic optimization to automatically compute the best controller gains and references, as well as the times for switching between the different controllers. We demonstrate this system through numerical simulations, validation in a physics-based simulation environment, as well as on a novel two-wheeled platform. The results show that the generated control strategies are successful in mission planning for this challenging problem domain and offer significant advantages over hand-tuned alternative

    @inproceedings{teeyapan2010limbo,
      title = {Robot Limbo: Optimized Planning and Control for Dynamically Stable Robots Under Vertical Obstacles},
      pages = {4519--4524},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Teeyapan, Kasemsit and Jiuguang Wang and Tobias Kunz and Mike Stilman},
      year = {2010}
    }
    
  • 2009
  • Jiuguang Wang, Philip Rogers, Lonnie Parker, Douglas Brooks, and Mike Stilman Robot Jenga: Autonomous and Strategic Block Extraction IEEE/RSJ International Conference on Intelligent Robots and Systems. 2009.

    This paper describes our successful implementation of a robot that autonomously and strategically removes multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that achieves repeatable performance in this challenging physical domain. In contrast to previous implementations, we rely only on low-cost, readily available system components and use strategic algorithms to resolve system uncertainty. We present a three-stage planner for block extraction which considers block selection, extraction order, and physics-based simulation that evaluates removability. Existing vision techniques are combined in a novel sequence for the identification and tracking of blocks within the tower. Discussion of our approach is presented following experimental results on a 5-DOF robot manipulator

    @inproceedings{wang2009jenga,
      title = {Robot Jenga: Autonomous and Strategic Block Extraction},
      pages = {5248--5253},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Jiuguang Wang and Rogers, Philip and Parker, Lonnie and Brooks, Douglas and Mike Stilman},
      year = {2009}
    }
    
  • Mike Stilman, Jiuguang Wang, Kasemsit Teeyapan, and Ray Marceau Optimized Control Strategies for Wheeled Humanoids and Mobile Manipulators IEEE/RAS International Conference on Humanoid Robotics. 2009. Best Paper Finalist

    Optimizing the control of articulated mobile robots leads to emergent behaviors that improve the effectiveness, efficiency and stability of wheeled humanoids and dynamically stable mobile manipulators. Our simulated results show that optimization over the target pose, height and control parameters results in effective strategies for standing, acceleration and deceleration. These strategies improve system performance by orders of magnitude over existing controllers. This paper presents a simple controller for robot motion and an optimization method for choosing its parameters. By using whole-body articulation, we achieve new skills such as standing and unprecedented levels of performance for acceleration and deceleration of the robot base. We describe a new control architecture, present a method for optimization, and illustrate its functionality through two distinct methods of simulation

    @inproceedings{stilman2009optimized,
      title = {Optimized Control Strategies for Wheeled Humanoids and Mobile Manipulators},
      pages = {568--573},
      month = {December},
      booktitle = {IEEE/RAS International Conference on Humanoid Robotics},
      author = {Mike Stilman and Jiuguang Wang and Teeyapan, Kasemsit and Marceau, Ray},
      year = {2009}
    }
    
  • 2008
  • Mike Stilman, Koichi Nishiwaki, and Satoshi Kagami Humanoid Teleoperation For Whole Body Manipulation IEEE International Conference on Robotics and Automation. 2008.

    We present results of successful telemanipulation of large, heavy objects by a humanoid robot. Using a single joystick the operator controls walking and whole body manipulation along arbitrary paths for up to ten minutes of continuous execution. The robot grasps, walks, pushes, pulls, turns and re-grasps a 55kg range of loads on casters. Our telemanipulation framework changes reference frames online to let the operator steer the robot in free walking, its hands in grasping and the object during mobile manipulation. In the case of manipulation, our system computes a robot motion that satisfies the commanded object path as well as the kinematic and dynamic constraints of the robot. Furthermore, we achieve increased robot stability by learning dynamic friction models of manipulated objects

    @inproceedings{stilman2008humanoid,
      title = {Humanoid Teleoperation For Whole Body Manipulation},
      pages = {3175--3180},
      month = {May},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Mike Stilman and Nishiwaki, Koichi and Satoshi Kagami},
      year = {2008}
    }
    
  • Jur van den Berg, Mike Stilman, James Kuffner, Ming Lin, and Dinesh Manocha Path Planning Among Movable Obstacles: A Probabilistically Complete Approach Workshop on the Algorithmic Foundation of Robotics. 2008.

    In this paper we study the problem of path planning among movable obstacles, in which a robot is allowed to move the obstacles if they block the robot's way from a start to a goal position. We make the observation that we can decouple the computations of the robot motions and the obstacle movements, and present a probabilistically complete algorithm, something which to date has not been achieved for this problem. Our algorithm maintains an explicit representation of the robot's configuration space. We present an eficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios

    @inproceedings{vandenberg2008path,
      title = {Path Planning Among Movable Obstacles: A Probabilistically Complete Approach},
      pages = {599--614},
      month = {December},
      booktitle = {Workshop on the Algorithmic Foundation of Robotics},
      author = {Jur van den Berg and Mike Stilman and James Kuffner and Lin, Ming and Manocha, Dinesh},
      year = {2008}
    }
    
  • 2007
  • Mike Stilman, Koichi Nishiwaki, and Satoshi Kagami Learning Object Models for Humanoid Manipulation IEEE/RAS International Conference on Humanoid Robotics. 2007.

    We present a successful implementation of rigid grasp manipulation for large objects moved along specified trajectories by a humanoid robot. HRP-2 manipulates tables on casters with a range of loads up to its own mass. The robot maintains dynamic balance by controlling its center of gravity to compensate for refiected forces. To achieve high performance for large objects with unspecified dynamics the robot learns a friction model for each object and applies it to torso trajectory generation. We empirically compare this method to a purely reactive strategy and show a significant increase in predictive power and stability.

    @inproceedings{stilman2007learning,
      title = {Learning Object Models for Humanoid Manipulation},
      pages = {174--179},
      month = {November},
      booktitle = {IEEE/RAS International Conference on Humanoid Robotics},
      author = {Mike Stilman and Nishiwaki, Koichi and Satoshi Kagami},
      year = {2007}
    }
    
  • Mike Stilman, Jan-Ullrich Schamburek, James Kuffner, and Tamim Asfour Manipulation planning among movable obstacles IEEE International Conference on Robotics and Automation. 2007.

    This paper presents the ResolveSpatialConstraints (RSC) algorithm for manipulation planning in a domain with movable obstacles. Empirically we show that our algorithm quickly generates plans for simulated articulated robots in a highly nonlinear search space of exponential dimension. RSC is a reverse-time search that samples future robot actions and constrains the space of prior object displacements. To optimize the efficiency of RSC, we identify methods for sampling object surfaces and generating connecting paths between grasps and placements. In addition to experimental analysis of RSC, this paper looks into object placements and task-space motion constraints among other unique features of the three dimensional manipulation planning domain.

    @inproceedings{stilman2007manipulation,
      title = {Manipulation planning among movable obstacles},
      pages = {3327--3332},
      month = {April},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Mike Stilman and Schamburek, Jan-Ullrich and James Kuffner and Tamim Asfour},
      year = {2007}
    }
    
  • Mike Stilman Task Constrained Motion Planning in Robot Joint Space IEEE/RSJ International Conference on Intelligent Robots and Systems. 2007.

    We explore global randomized joint space path planning for articulated robots that are subject to task space constraints. This paper describes a representation of constrained motion for joint space planners and develops two simple and efficient methods for constrained sampling of joint configurations: Tangent Space Sampling (TS) and First-Order Retraction (FR). Constrained joint space planning is important for many real world problems involving redundant manipulators. On the one hand, tasks are designated in work space coordinates: rotating doors about fixed axes, sliding drawers along fixed trajectories or holding objects level during transport. On the other, joint space planning gives alternative paths that use redundant degrees of freedom to avoid obstacles or satisfy additional goals while performing a task. In simulation, we demonstrate that our methods are faster and significantly more invariant to problem/algorithm parameters than existing techniques

    @inproceedings{stilman2007task,
      title = {Task Constrained Motion Planning in Robot Joint Space},
      pages = {3074--3081},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Mike Stilman},
      year = {2007}
    }
    
  • Satoshi Kagami, Koichi Nishiwaki, James Kuffner, Simon Thompson, Joel Chestnutt, Mike Stilman, and Philipp Michel Humanoid HRP2-DHRC Autonomous and Interactive Behavior Robotics Research: Results of the 12th International Symposium ISRR. Ch. 11. Ed. Thrun, S., Brooks, R.A., and Durrant-Whyte, H.F.. Springer Verlag. 2007.

    Recently, research on humanoid-type robots has become increasingly active, and a broad array of fundamental issues are under investigation. However, in order to achieve a humanoid robot which can operate in human environ- ments, not only the fundamental components themselves, but also the suc- cessful integration of these components will be required. At present, almost all humanoid robots that have been developed have been designed for bipedal locomotion experiments. In order to satisfy the functional demands of loco- motion as well as high-level behaviors, humanoid robots require good me- chanical design, hardware, and software which can support the integration of tactile sensing, visual perception, and motor control. Autonomous behaviors are currently still very primitive for humanoid-type robots. It is difficult to conduct research on high-level autonomy and intelligence in humanoids due to the development and maintenance costs of the hardware. We believe low- level autonomous functions will be required in order to conduct research on higher-level autonomous behaviors for humanoids.

    @inproceedings{kagami2007humanoid,
      title = {Humanoid HRP2-DHRC Autonomous and Interactive Behavior},
      pages = {103--117},
      publisher = {Springer Verlag},
      chapter = {11},
      editor = {Thrun, S. and Brooks, R.A. and Durrant-Whyte, H.F.},
      booktitle = {Robotics Research: Results of the 12th International Symposium ISRR},
      author = {Satoshi Kagami and Nishiwaki, Koichi and James Kuffner and Thompson, Simon and Chestnutt, Joel and Mike Stilman and Michel, Philipp},
      year = {2007}
    }
    
  • 2006
  • Mike Stilman, Koichi Nishiwaki, Satoshi Kagami, and James Kuffner Planning and Executing Navigation Among Movable Obstacles IEEE/RSJ International Conference on Intelligent Robots and Systems. 2006.

    This paper explores autonomous locomotion, reaching, grasping and manipulation for the domain of Navigation Among Movable Obstacles (NAMO). The robot perceives and constructs a model of an environment filled with various fixed and movable obstacles, and automatically plans a navigation strategy to reach a desired goal location. The planned strategy consists of a sequence of walking and compliant manipulation operations. It is executed by the robot with online feedback. We give an overview of our NAMO system, as well as provide details of the autonomous planning, online grasping and compliant hand positioning during dynamically-stable walking. Finally, we present results of a successful implementation running on the Humanoid Robot HRP-2

    @inproceedings{stlman2006planning,
      title = {Planning and Executing Navigation Among Movable Obstacles},
      pages = {1617--1634},
      month = {October},
      booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
      author = {Mike Stilman and Nishiwaki, Koichi and Satoshi Kagami and James Kuffner},
      year = {2006}
    }
    
  • Mike Stilman and James Kuffner Planning Among Movable Obstacles with Artificial Constraints Workshop on the Algorithmic Foundations of Robotics. 2006.

    This paper presents artiificial constraints as a method for guiding heuristic search in the computationally challenging domain of motion planning among movable obstacles. The robot is permitted to manipulate unspecifieed obstacles in order to create space for a path. A plan is an ordered sequence of paths for robot motion and object manipulation. We show that under monotone assumptions, anticipating future manipulation paths results in constraints on both the choice of objects and their placements at earlier stages in the plan. We present an algorithm that uses this observation to incrementally reduce the search space and quickly find solutions to previously unsolved classes of movable obstacle problems. Our planner is developed for arbitrary robot geometry and kinematics. It is presented with an implementation for the domain of navigation among movable obstacles.

    @inproceedings{stilman2006planningamong,
      title = {Planning Among Movable Obstacles with Artificial Constraints},
      pages = {1295--1307},
      month = {July},
      booktitle = {Workshop on the Algorithmic Foundations of Robotics},
      author = {Mike Stilman and James Kuffner},
      year = {2006}
    }
    
  • 2005
  • Mike Stilman, Chris Atkeson, James Kuffner, and Garth Zeglin Dynamic Programming in Reduced Dimensional Spaces: Dynamic Planning For Robust Biped Locomotion IEEE International Conference on Robotics and Automation. 2005.

    We explore the use of computational optimal control techniques for automated construction of policies in complex dynamic environments. Our implementation of dynamic programming is performed in a reduced dimensional subspace of a simulated four-DOF biped robot with point feet. We show that a computed solution to this problem can be generated and yield empirically stable walking that can handle various types of disturbances.

    @inproceedings{stilman2005dynamic,
      title = {Dynamic Programming in Reduced Dimensional Spaces: Dynamic Planning For Robust Biped Locomotion},
      pages = {2399--2404},
      month = {April},
      booktitle = {IEEE International Conference on Robotics and Automation},
      author = {Mike Stilman and Chris Atkeson and James Kuffner and Zeglin, Garth},
      year = {2005}
    }
    
  • 2004
  • Mike Stilman and James Kuffner Navigation Among Movable Obstacles: Real-time Reasoning in Complex Environments IEEE/RAS International Conference on Humanoid Robotics. 2004.

    In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multiobject domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.

    @inproceedings{stilman2004navigation,
      title = {Navigation Among Movable Obstacles: Real-time Reasoning in Complex Environments},
      pages = {322--341},
      month = {November},
      booktitle = {IEEE/RAS International Conference on Humanoid Robotics},
      author = {Mike Stilman and James Kuffner},
      year = {2004}
    }
    

Workshop

  • Arash Rouhani, Neil T. Dantam, and Mike Stilman Software-Synthesis via LL(*) for Context-Free Robot Programs 4th Workshop on Formal Methods for Robotics and Automation, RSS. 2013.

    Producing reliable software for robotic systems requires formal techniques to ensure correctness. Some popular approaches model the discrete dynamics and computation of the robot using finite state automata or linear temporal logic. We can represent more complicated systems and tasks, and still retain key guarantees on verifiability and runtime performance, by modeling the system instead with a context-free grammar. The challenge with a context-free model is the need for a more advanced software synthesis algorithm. We address this challenge by adapting the LL(*) parser generation algorithm, originally developed for program translation, to the domain of online robot control. We demonstrate this LL(*) parser generation implementation in the Motion Grammar Kit, permitting synthesis for robot control software for complex, hierarchical, and recursive tasks.

    @inproceedings{rouhani2013software,
      title = {Software-Synthesis via LL(*) for Context-Free Robot Programs},
      month = {June},
      booktitle = {4th Workshop on Formal Methods for Robotics and Automation, RSS},
      author = {Rouhani, Arash and Neil T. Dantam and Mike Stilman},
      year = {2013}
    }
    
  • Neil T. Dantam, Magnus Egerstedt, and Mike Stilman Make Your Robot Talk Correctly: Deriving Models of Hybrid System RSS Workshop on Grounding Human-Robot Dialog for Spatial Tasks. 2011.

    Using both formal language and differential equations to model a robotic system, we introduce a calculus of transformation rules for the symbolic derivation of hybrid controllers. With a Context-Free Motion Grammar, we show how to test reachability between different regions of state-space and give several symbolic transformations to modify the set of event strings the system may generate. This approach lets one modify the language of the hybrid system, providing a way to change system behavior so that it satisfies linguistic constraints on correct operation.

    @inproceedings{dantam2011talk,
      title = {Make Your Robot Talk Correctly: Deriving Models of Hybrid System},
      month = {June},
      booktitle = {RSS Workshop on Grounding Human-Robot Dialog for Spatial Tasks},
      author = {Neil T. Dantam and Magnus Egerstedt and Mike Stilman},
      year = {2011}
    }
    
  • Victor Emeli, Charlie Kemp, and Mike Stilman Push Planning for Object Placement in Clutter Using the PR-2. The PR2 Workshop, IEEE Int. Conf. on Intelligent Robots and Systems. 2011.

    The goal of this project is to investigate the implementation of a planning algorithm for the problem of placing objects on a cluttered surface with a PR-2 mobile manipulator. The original push planning algorithm was initially developed as a simulation. We modified the simulator for execution in real-world cluttered environments. This paper discusses the challenges of implementation and presents empirical results that determine how well the simulator models the real world as clutter is pushed and collides with other objects

    @inproceedings{emeli2011push,
      title = {Push Planning for Object Placement in Clutter Using the PR-2.},
      month = {September},
      booktitle = {The PR2 Workshop, IEEE Int. Conf. on Intelligent Robots and Systems},
      author = {Emeli, Victor and Charlie Kemp and Mike Stilman},
      year = {2011}
    }
    

Technical Reports

  • Rowland O'Flaherty, Pete Vieira, M.X. Grey, Paul Oh, Aaron Bobick, Magnus Egerstedt, and Mike Stilman Kinematics and Inverse Kinematics for the Humanoid Robot HUBO2+ no. GT-GOLEM-2013-001. Georgia Institute of Technology, Atlanta, GA. 2013.

    This paper derives the forward and inverse kinematics of a humanoid robot. The specific humanoid that the derivation is for is a robot with 27 degrees of freedom but the procedure can be easily applied to other similar humanoid platforms. First, the forward and inverse kinematics are derived for the arms and legs. Then, the kinematics for the torso and the head are solved. Finally, the forward and inverse kinematic solutions for the whole body are derived using the kinematics of arms, legs, torso, and head.

    @techreport{oflaherty2013hubokinematics,
      title = {Kinematics and Inverse Kinematics for the Humanoid Robot HUBO2+},
      number = {GT-GOLEM-2013-001},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Rowland O'Flaherty and Pete Vieira and M.X. Grey and Oh, Paul and Bobick, Aaron and Magnus Egerstedt and Mike Stilman},
      year = {2013}
    }
    
  • Martin Levihn, Matthew Dutton, Alexander Trevor, and Mike Stilman Detecting Partially Occluded Objects via Segmentation and Validation no. GT-GOLEM-2012-001. Georgia Institute of Technology, Atlanta, GA. 2012.

    This paper presents a novel algorithm: Verfied Partial Object Detector (VPOD) for accurate detection of partially occluded objects such as furniture in 3D point clouds. VPOD is implemented and validated on real sensor data obtained by our robot. It extends Viewpoint Feature Histograms (VFH) which classify unoccluded objects to also classifying partially occluded objects such as furniture that might be seen in typical office environments. To achieve this result, VPOD employs two strategies. First, object models are segmented and the object database is extended to include partial models. Second, once a matching partial object is detected, the full object model is aligned back into the scene and verified for consistency with the point cloud data. Overall, our approach increases the number of objects found and substantially reduces false positives due to the verification process.

    @techreport{levihn2012detecting,
      title = {Detecting Partially Occluded Objects via Segmentation and Validation},
      number = {GT-GOLEM-2012-001},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Martin Levihn and Dutton, Matthew and Alexander Trevor and Mike Stilman},
      year = {2012}
    }
    
  • Neil T. Dantam, Irfan Essa, and Mike Stilman Algorithms for Linguistic Robot Policy Inference from Demonstration of Assembly Tasks no. GT-GOLEM-2012-002. Georgia Institute of Technology, Atlanta, GA. 2012.

    We describe several algorithms used for the inference of linguistic robot policies from human demonstration. First, tracking and match objects using the Hungarian Algorithm. Then, we convert Regular Expressions to Nondeterministic Finite Automata (NFA) using the McNaughton-Yamada-Thompson Algorithm. Next, we use Subset Construction to convert to a Deterministic Finite Automaton. Finally, we minimize finite automata using either Hopcroft's Algorithm or Brzozowski's Algorithm.

    @techreport{dantam2012algorithms,
      title = {Algorithms for Linguistic Robot Policy Inference from Demonstration of Assembly Tasks},
      number = {GT-GOLEM-2012-002},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Neil T. Dantam and Irfan Essa and Mike Stilman},
      year = {2012}
    }
    
  • Tobias Kunz and Mike Stilman Turning Paths Into Trajectories Using Parabolic Blends no. GT-GOLEM-2011-004. Georgia Institute of Technology, Atlanta, GA. 2011.

    We present an approach for converting a path of multiple continuous linear segments into a trajectory that satisfies velocity and acceleration constraints and closely follows the given path without coming to a complete stop at every waypoint. Our method applies parabolic blends around waypoints to improve speed. In contrast to established methods that smooth trajectories with parabolic blends, our method does not require the timing of waypoints or durations of blend phases. This makes our approach particularly useful for robots that must follow kinematic paths that are not explicitly parametrized by time. Our method chooses timing automatically to achieve high performance while satisfying the velocity and acceleration constraints of a given robot.

    @techreport{kunz2011turning,
      title = {Turning Paths Into Trajectories Using Parabolic Blends},
      number = {GT-GOLEM-2011-004},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Tobias Kunz and Mike Stilman},
      year = {2011}
    }
    
  • Tobias Kunz and Mike Stilman Time-Optimal Path Following with Bounded Joint Accelerations and Velocities no. GT-GOLEM-2011-005. Georgia Institute of Technology, Atlanta, GA. 2011.

    This paper presents a method to generate the time-optimal trajectroy that exactly follows a given differentiable joint-space path within given bounds on joint accelerations and velocities. We also present a path preprocessing method to make nondifferentiable paths differentiable by adding circular blends.

    @techreport{kunz2011time,
      title = {Time-Optimal Path Following with Bounded Joint Accelerations and Velocities},
      number = {GT-GOLEM-2011-005},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Tobias Kunz and Mike Stilman},
      year = {2011}
    }
    
  • Martin Levihn and Mike Stilman Efficient Opening Detection no. GT-GOLEM-2011-002. Georgia Institute of Technology. 2011.

    We present an efficient and powerful algorithm for detecting openings. Openings indicate the existence of a new path for the robot. The reliable detection of new openings is of great relevance for the domain of moving objects as a robot typically needs to detect openings for itself to navigate through. It is also especially relevant to the domain of Navigation Among Movable Obstacles in known as well as unknown environments. In these domains a robot has to plan for object manipulations that help it to navigate to the goal. Tremendous speed-ups for algorithms in these domains can be achieved by limiting the considerations of obstacle manipulations to cases where manipulations create new openings. The presented algorithm can detect openings for obstacles of arbitrary shapes being displaced or moving by themselves, in arbitrarily directions in changing environments. To the knowledge of the authors, this is the first algorithm to achieve efficient opening detection for arbitrary shaped obstacles.

    @techreport{levihn2011efficient,
      title = {Efficient Opening Detection},
      number = {GT-GOLEM-2011-002},
      institution = {Georgia Institute of Technology},
      author = {Martin Levihn and Mike Stilman},
      year = {2011}
    }
    
  • Neil T. Dantam and Mike Stilman Ach: IPC for Real-Time Robot Control no. GT-GOLEM-2011-001. Georgia Institute of Technology, Atlanta, GA. 2011.

    We present a new Inter-Process Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating perception, control drivers, and algorithms in real-time systems that sample data from physical processes. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems. Finally, the source code for Ach is available under an Open Source BSD-style license.

    @techreport{dantam2011achtech,
      title = {Ach: IPC for Real-Time Robot Control},
      number = {GT-GOLEM-2011-001},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Neil T. Dantam and Mike Stilman},
      year = {2011}
    }
    
  • Saul Reynolds-Haertle and Mike Stilman Design and Development of a Dynamically-Balancing Holonomic Robot no. GT-GOLEM-2011-003. Georgia Institute of Technology, Atlanta, GA. 2011.

    This paper describes the design, control, and construction of Golem Wing, the first vehicle which both balances dynamically and has entirely holonomic ground movement. A nonstandard linear arrangement of mecanum wheels gives it the load-lifting, performance, and manipulation benefits of a dynamically-balancing platform without the maneuvering difficulties exhibited by previous balancing platforms. We show that the arrangement is capable of holonomic motion, describe a controller that maintains dynamic balance during holonomic motion, and show an implementation of the system in hardware that validate our assertions.

    @techreport{reynolds2011design,
      title = {Design and Development of a Dynamically-Balancing Holonomic Robot},
      number = {GT-GOLEM-2011-003},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Saul Reynolds-Haertle and Mike Stilman},
      year = {2011}
    }
    
  • Neil T. Dantam and Mike Stilman The Motion Grammar: Linguistic Perception, Planning, and Control no. GT-GOLEM-2010-001. Georgia Institute of Technology, Atlanta, GA. 2010.

    We present the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and hybrid control that provides a computationally tractable way to control robots in uncertain environments with guarantees on completeness and correctness. The grammar represents a policy for the task which is parsed in real-time based on perceptual input. Branches of the syntax tree form the levels of a hierarchical decomposition, and the individual robot sensor readings are given by tokens. We implement this approach in the interactive game of Yamakuzushi on a physical robot resulting in a system that repeatably competes with a human opponent in sustained game-play for matches up to six minutes.

    @techreport{dantam2010mgtech,
      title = {The Motion Grammar: Linguistic Perception, Planning, and Control},
      number = {GT-GOLEM-2010-001},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Neil T. Dantam and Mike Stilman},
      year = {2010}
    }
    
  • Neil T. Dantam, Pushkar Kolhe, and Mike Stilman Equations of Motion for Dynamically Stable Mobile Manipulators no. GT-GOLEM-2010-002. Georgia Institute of Technology, Atlanta, GA. 2010.

    Equations of motion for dynamically stable mobile manipulation

    @techreport{dantam2010equations,
      title = {Equations of Motion for Dynamically Stable Mobile Manipulators},
      number = {GT-GOLEM-2010-002},
      institution = {Georgia Institute of Technology, Atlanta, GA},
      author = {Neil T. Dantam and Pushkar Kolhe and Mike Stilman},
      year = {2010}
    }
    
  • Mike Stilman, Philipp Michel, Joel Chestnutt, Koichi Nishiwaki, Satoshi Kagami, and James Kuffner Augmented Reality for Robot Development and Experimentation no. CMU-RI-TR-05-55. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. 2005.

    The successful development of autonomous robotic systems requires careful fusion of complex subsystems for perception, planning, and control. Often these subsystems are designed in a modular fashion and tested individually. However, when ultimately combined with other components to form a complete system, unexpected interactions between subsystems can occur that make it difficult to isolate the source of problems. This paper presents a novel paradigm for robot experimentation that enables unified testing of individual subsystems while acting as part of a complete whole made up of both virtual and real components. We exploit the recent advances in speed and accuracy of optical motion capture to localize the robot, track environment objects, and extract extrinsic parameters for moving cameras in real-time. We construct a world model representation that serves as ground truth for both visual and tactile sensors in the environment. From this data, we build spatial and temporal correspondences between virtual elements, such as motion plans, and real artifacts in the scene. The system enables safe, decoupled testing of component algorithms for vision, motion planning and control that would normally have to be tested simultaneously on actual hardware. We show results of successful online applications in the development of an autonomous humanoid robot.

    @techreport{stilman2005augmented,
      title = {Augmented Reality for Robot Development and Experimentation},
      number = {CMU-RI-TR-05-55},
      institution = {Robotics Institute, Carnegie Mellon University, Pittsburgh, PA},
      author = {Mike Stilman and Michel, Philipp and Chestnutt, Joel and Nishiwaki, Koichi and Satoshi Kagami and James Kuffner},
      year = {2005}
    }
    
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