用模块化残差学习提升模型控制在不确定环境下的稳定性。
A Modular Residual Learning Framework to Enhance Model-Based Approach for Robust Locomotion
- 将学习模块嵌入模型控制的各部分，补偿模型不匹配问题。
- 实机四足机器人在复杂环境中保持平衡并精准追踪速度指令。
- 适合需要高鲁棒性的机器人控制场景，尤其对参数调优更宽容。
本文提出一种结合模型驱动与学习驱动优势的新方法，以实现鲁棒运动控制。通过将残差模块与基于启发式设计的步态规划器和动力学模型对应集成，弥补因模型失配导致的性能下降。采用模块化结构，并为每个残差模块选择合适的学习方法，使系统在高不确定性环境下控制表现更优，且学习效率高于基线方法。此外，该方法不仅提升控制性能，还增强了常规控制器对参数调优的鲁棒性。为验证可行性，我们将在真实四足机器人上结合模型预测控制的残差模块进行测试。尽管存在超出仿真范围的不确定性，机器人仍成功维持平衡并精确跟踪设定速度。
原文摘要 · Abstract (English)
This paper presents a novel approach that combines the advantages of both model-based and learning-based frameworks to achieve robust locomotion. The residual modules are integrated with each corresponding part of the model-based framework, a footstep planner and dynamic model designed using heuristics, to complement performance degradation caused by a model mismatch. By utilizing a modular structure and selecting the appropriate learning-based method for each residual module, our framework demonstrates improved control performance in environments with high uncertainty, while also achieving higher learning efficiency compared to baseline methods. Moreover, we observed that our proposed methodology not only enhances control performance but also provides additional benefits, such as making nominal controllers more robust to parameter tuning. To investigate the feasibility of our framework, we demonstrated residual modules combined with model predictive control in a real quadrupedal robot. Despite uncertainties beyond the simulation, the robot successfully maintains balance and tracks the commanded velocity.
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