TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation

@inproceedings{chen_trajectotree_2021,
title = {{TrajectoTree}: {Trajectory} {Optimization} {Meets} {Tree} {Search} for {Planning} {Multi}-contact {Dexterous} {Manipulation}},
abstract = {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.},
booktitle = {2021 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},
author = {Chen, Claire and Culbertson, Preston and Lepert, Marion and Schwager, Mac and Bohg, Jeannette},
month = nov,
year = {2021},
pages = {8262--8268},
month_numeric = {11}
}