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.

翻译：对于接触丰富的操作任务，什么是接触动力学的良好局部描述？我们可以在何处信任这一局部描述？尽管许多方法通常依赖于具有椭球信任区域的动力学泰勒近似，但我们认为这类方法与接触的单向性本质存在根本性矛盾。为此，我们提出了接触信任区域（CTR），该模型既能捕捉接触的单向特性，又能保持计算效率。基于CTR，我们首先开发了一种能够合成局部接触丰富操作计划的模型预测控制（MPC）算法。随后，通过将局部MPC计划进行拼接，我们将该能力扩展至全局规划层面，从而实现高效且灵巧的接触丰富操作。为验证方法的性能，我们在高保真仿真与硬件平台上对两个接触丰富系统进行了全面评估：平面IiwaBimanual系统与三维AllegroHand系统。实验表明，相较于现有基于强化学习的接触丰富操作方法，我们的方法在计算资源消耗方面显著降低。特别值得指出的是，我们以路线图形式实现的Allegro手内操作策略，在标准笔记本电脑仅使用CPU的情况下，离线构建时间不足10分钟，在线推理仅需数秒。实验数据、视频及代码可通过ctr.theaiinstitute.com获取。