TrajectoTree

Dexterous manipulation tasks often require contact switching, where fingers make and break contact with the object. We propose a method that plans trajectories for dexterous manipulation tasks involving contact switching using contact-implicit trajectory optimization (CITO) augmented with a high-level discrete contact sequence planner. We first use the high-level planner to find a sequence of finger contact switches given a desired object trajectory. With this contact sequence plan, we impose additional constraints in the CITO problem. We show that our method finds trajectories approximately 7 times faster than a general CITO baseline for a four-finger planar manipulation scenario. Furthermore, when executing the planned trajectories in a full dynamics simulator, we are able to more closely track the object pose trajectories planned by our method than those planned by the baselines.

arXiv link: https://arxiv.org/abs/2109.14088

BibTex:

@article{Chen2021TrajectoTreeTO,

title={TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation},

author={Claire Chen and Preston Culbertson and Marion Lepert and Mac Schwager and Jeannette Bohg},

journal={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

year={2021},

pages={8262-8268}

}