面向人形机器人的视觉灵巧操作：从仿真到现实的强化学习

Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

February 27, 2025

作者: Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu

cs.AI

摘要

强化学习在实现人类乃至超人类水平的能力方面，已在多种问题领域展现出令人瞩目的成果，但在灵巧机器人操控方面的成功仍显有限。本研究探讨了将强化学习应用于解决人形机器人上的一系列接触密集型操控任务时所面临的关键挑战。我们引入了一系列创新技术，通过实证验证来克服这些已识别的挑战。我们的主要贡献包括：一个自动化的真实到模拟调优模块，使模拟环境更贴近现实世界；一个通用的奖励设计方案，简化了针对长期接触密集型操控任务的奖励工程；一种分而治之的蒸馏过程，在保持模拟到现实性能的同时，提高了硬探索问题的样本效率；以及稀疏与密集物体表示的混合使用，以弥合模拟到现实的感知差距。我们在三项人形灵巧操控任务上展示了积极的结果，并对每项技术进行了消融研究。我们的工作展示了一种成功利用模拟到现实强化学习来掌握人形灵巧操控的方法，实现了强大的泛化能力和高性能，而无需依赖人类示范。

English

Reinforcement learning has delivered promising results in achieving human- or even superhuman-level capabilities across diverse problem domains, but success in dexterous robot manipulation remains limited. This work investigates the key challenges in applying reinforcement learning to solve a collection of contact-rich manipulation tasks on a humanoid embodiment. We introduce novel techniques to overcome the identified challenges with empirical validation. Our main contributions include an automated real-to-sim tuning module that brings the simulated environment closer to the real world, a generalized reward design scheme that simplifies reward engineering for long-horizon contact-rich manipulation tasks, a divide-and-conquer distillation process that improves the sample efficiency of hard-exploration problems while maintaining sim-to-real performance, and a mixture of sparse and dense object representations to bridge the sim-to-real perception gap. We show promising results on three humanoid dexterous manipulation tasks, with ablation studies on each technique. Our work presents a successful approach to learning humanoid dexterous manipulation using sim-to-real reinforcement learning, achieving robust generalization and high performance without the need for human demonstration.