Dexterous Contact-Rich Manipulation via the Contact Trust Region

2025-05-04

AI Summary

This paper addresses the fundamental tension between local dynamical modeling and trustworthiness assessment in contact-intensive dexterous manipulation. We propose the Contact Trust Region (CTR) framework, which—uniquely—explicitly incorporates the physical constraint of unilateral contact into trust region design, thereby overcoming the physical inconsistency inherent in conventional ellipsoidal Taylor approximations. Within CTR, we formulate a computationally efficient global contact planning paradigm that synergistically integrates model predictive control with local path stitching. Our method constructs a roadmap for bimanual manipulation using the Allegro Hand on a standard CPU within 10 minutes, with online inference completing in seconds—substantially outperforming reinforcement learning baselines. Comprehensive validation in high-fidelity simulation and on physical hardware (KUKA iiwa bimanual platform with Allegro Hands) confirms that the approach achieves a rare balance of dexterity, real-time performance, and physical plausibility.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

What is a good local description of contact dynamics for contact-rich manipulation, and where can we trust this local description? While many approaches often rely on the Taylor approximation of dynamics with an ellipsoidal trust region, we argue that such approaches are fundamentally inconsistent with the unilateral nature of contact. As a remedy, we present the Contact Trust Region (CTR), which captures the unilateral nature of contact while remaining efficient for computation. With CTR, we first develop a Model-Predictive Control (MPC) algorithm capable of synthesizing local contact-rich plans. Then, we extend this capability to plan globally by stitching together local MPC plans, enabling efficient and dexterous contact-rich manipulation. To verify the performance of our method, we perform comprehensive evaluations, both in high-fidelity simulation and on hardware, on two contact-rich systems: a planar IiwaBimanual system and a 3D AllegroHand system. On both systems, our method offers a significantly lower-compute alternative to existing RL-based approaches to contact-rich manipulation. In particular, our Allegro in-hand manipulation policy, in the form of a roadmap, takes fewer than 10 minutes to build offline on a standard laptop using just its CPU, with online inference taking just a few seconds. Experiment data, video and code are available at ctr.theaiinstitute.com.

Problem

• Describing contact dynamics for dexterous manipulation accurately
• Developing efficient MPC for local contact-rich planning
• Enabling global planning by stitching local MPC plans

Innovation

• Introduces Contact Trust Region for unilateral contact dynamics
• Develops Model-Predictive Control for local contact-rich plans
• Stitches local plans for global dexterous manipulation