用李雅普诺夫指数优化机器人系统稳定性，提升复杂环境下的鲁棒性。
Enhancing Robotic System Robustness via Lyapunov Exponent-Based Optimization
- 基于李雅普诺夫指数设计可微分仿真优化框架，量化系统稳定性。
- 在高自由度与接触丰富的复杂场景中显著提升系统鲁棒性。
- 适用于运动控制、动力学设计等需要稳定性的机器人研究者。
我们提出一种基于李雅普诺夫指数的新方法，用于量化和优化机器人系统的稳定性，解决机器人分析、设计与优化中的开放性挑战。该方法利用长时间跨度的可微分仿真，其度量指标天然支持机器人任务中常见的极限环。通过自定义的JAX梯度优化框架，展示了强大的灵活性与性能。我们在多种复杂度的场景中验证了该方法的有效性，涵盖高自由度系统和接触丰富的环境。各类测试结果均表明，该方法在提升系统鲁棒性方面具有显著潜力。
原文摘要 · Abstract (English)
We present a novel approach to quantifying and optimizing stability in robotic systems based on the Lyapunov exponents addressing an open challenge in the field of robot analysis, design, and optimization. Our method leverages differentiable simulation over extended time horizons. The proposed metric offers several properties, including a natural extension to limit cycles commonly encountered in robotics tasks and locomotion. We showcase, with an ad-hoc JAX gradient-based optimization framework, remarkable power, and flexi-bility in tackling the robustness challenge. The effectiveness of our approach is tested through diverse scenarios of varying complexity, encompassing high-degree-of-freedom systems and contact-rich environments. The positive outcomes across these cases highlight the potential of our method in enhancing system robustness.
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。