# Enhancing Dexterity in Robotic Manipulation via Hierarchical Contact Exploration

arXiv: https://arxiv.org/abs/2307.00383 · HTML: https://mlsanity.com/paper/2307.00383/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2023.

## Built on by (most cited first, 10 of 10)

- [Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization](https://mlsanity.com/paper/2502.20382/) (2025)
- [Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation](https://mlsanity.com/paper/2408.01258/) (2024)
- [Learning Extrinsic Dexterity with Parameterized Manipulation Primitives](https://mlsanity.com/paper/2310.17785/) (2023)
- [Diffusion-Informed Probabilistic Contact Search for Multi-Finger Manipulation](https://mlsanity.com/paper/2410.00841/) (2024)
- [Learning Long-Horizon Robot Manipulation Skills via Privileged Action](https://mlsanity.com/paper/2502.15442/) (2025)
- [Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning](https://mlsanity.com/paper/2603.09882/) (2026)
- [On the Feasibility of A Mixed-Method Approach for Solving Long Horizon Task-Oriented Dexterous Manipulation](https://mlsanity.com/paper/2410.07403/) (2024)
- [Implicit Contact Diffuser: Sequential Contact Reasoning with Latent Point Cloud Diffusion](https://mlsanity.com/paper/2410.16571/) (2024)
- [CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation](https://mlsanity.com/paper/2502.00835/) (2025)
- [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)

Planning robot dexterity is challenging due to the non-smoothness introduced by contacts, intricate fine motions, and ever-changing scenarios. We present a hierarchical planning framework for dexterous robotic manipulation (HiDex). This framework explores in-hand and extrinsic dexterity by leveraging contacts. It generates rigid-body motions and complex contact sequences. Our framework is based on Monte-Carlo Tree Search and has three levels: 1) planning object motions and environment contact modes; 2) planning robot contacts; 3) path evaluation and control optimization. This framework offers two main advantages. First, it allows efficient global reasoning over high-dimensional complex space created by contacts. It solves a diverse set of manipulation tasks that require dexterity, both intrinsic (using the fingers) and extrinsic (also using the environment), mostly in seconds. Second, our framework allows the incorporation of expert knowledge and customizable setups in task mechanics and models. It requires minor modifications to accommodate different scenarios and robots. Hence, it provides a flexible and generalizable solution for various manipulation tasks. As examples, we analyze the results on 7 hand configurations and 15 scenarios. We demonstrate 8 tasks on two robot platforms.