Planning pre-grasp manipulation for transport tasks

Lillian Y. Chang, Siddhartha S. Srinivasa , Nancy S. Pollard



Studies of human manipulation strategies suggest that pre-grasp object manipulation, such as rotation or sliding of the object to be grasped, can improve task performance by increasing both the task success rate and the quality of load-supporting postures. In previous demonstrations, pre-grasp object rotation by a robot manipulator was limited to manually-programmed actions. We present a method for automating the planning of pre-grasp rotation for object transport tasks. Our technique optimizes the grasp acquisition point by selecting a target object pose that can be grasped by high-payload manipulator configurations. Careful selection of the transition states leads to successful transport plans for tasks that are otherwise infeasible. In addition, optimization of the grasp acquisition posture also indirectly improves the transport plan quality, as measured by the safety margin of the manipulator payload limits.

Publication

Planning pre-grasp manipulation for transport tasks.
Lillian Y. Chang, Siddhartha S. Srinivasa, Nancy S. Pollard.
IEEE International Conference on Robotics and Automation (ICRA), May 2010. Videos View paper video and additional video examples

Acknowledgements

The authors thank Dmitry Berenson for sharing his planner implementation and the entire Personal Robotics project team at Intel Labs Pittsburgh for discussion and help with the HERB system. We are also grateful to Moshe Mahler and James Chan for modeling the example scenes and rendering the simulation results. Autodesk donated the Maya software package used to render the 3-D animations. This work was supported by the National Science Foundation (CCF-0702443). The material is based upon work partially supported by the National Science Foundation under Grant No. EEC-0540865. L. Y. Chang received support from a NASA Harriet G. Jenkins Pre-Doctoral Fellowship.

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