融合强化学习与模型预测控制，提升四足机器人抓取与行走协同能力。
RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation
- 用强化学习训练反馈策略，结合模型预测生成前馈力矩
- 在真实场景中完成推购物车、端盘子等复杂任务
- 兼顾精确操控与动态行走鲁棒性，适合复杂交互任务
双足/四足机器人在执行同时涉及移动与操作的任务时，面临末端执行器精准控制和对未建模动力学鲁棒性的双重挑战。基于模型的控制器虽能通过在线优化实现高精度规划，但受限于模型误差；而学习型方法虽具备鲁棒性，却难以精确调节交互力。本文提出RAMBO，一种将模型驱动的全身控制嵌入强化学习反馈策略的混合框架。模型模块通过求解二次规划生成前馈力矩，策略网络提供反馈校正项以增强适应性。我们在真实四足机器人上验证了该框架在推购物车、平衡盘子、抓持软物等多样化任务中的表现，涵盖双足与四足行走模式。实验表明，RAMBO可在保持动态稳定行走的同时实现高精度操作能力。
原文摘要 · Abstract (English)
Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both precise end-effector control and robustness to unmodeled dynamics. While model-based controllers provide precise planning via online optimization, they are limited by model inaccuracies. In contrast, learning-based methods offer robustness, but they struggle with precise modulation of interaction forces. We introduce RAMBO, a hybrid framework that integrates model-based whole-body control within a feedback policy trained with reinforcement learning. The model-based module generates feedforward torques by solving a quadratic program, while the policy provides feedback corrective terms to enhance robustness. We validate our framework on a quadruped robot across a diverse set of real-world loco-manipulation tasks, such as pushing a shopping cart, balancing a plate, and holding soft objects, in both quadrupedal and bipedal walking. Our experiments demonstrate that RAMBO enables precise manipulation capabilities while achieving robust and dynamic locomotion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。