TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation
This addresses the challenge of efficient planning for multi-contact dexterous manipulation tasks, which is incremental as it builds on existing trajectory optimization methods.
The paper tackles the problem of planning dexterous manipulation trajectories with contact switching by combining contact-implicit trajectory optimization with a high-level discrete contact sequence planner, resulting in trajectories found approximately 7 times faster than a baseline and improved tracking in simulation.
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.