Combining Motion Planning and Optimization for Flexible Robot Manipulation |
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.
Project Members:
Mike Stilman |
Publications:
J. Scholz and M. Stilman. Combining Motion Planning and Optimization for Flexible Robot Manipulation. In IEEE-RAS International Conference on Humanoid Robotics, 2010. (Best Paper Award)
