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

arXiv: https://arxiv.org/abs/2109.14088 · HTML: https://mlsanity.com/paper/2109.14088/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2021.

## Built on by (most cited first, 5 of 5)

- [Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes](https://mlsanity.com/paper/2211.11744/) (2022)
- [Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllers](https://mlsanity.com/paper/2303.03533/) (2023)
- [Diffusion-Informed Probabilistic Contact Search for Multi-Finger Manipulation](https://mlsanity.com/paper/2410.00841/) (2024)
- [Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation](https://mlsanity.com/paper/2310.04930/) (2023)
- [Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets](https://mlsanity.com/paper/2601.10827/) (2026)

## Abstract (arXiv)

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.