用真实数据优化四足机器人耗电，实测省电24%-28%。
Fine-Tuning Hard-to-Simulate Objectives for Quadruped Locomotion: A Case Study on Total Power Saving
- 用真实世界数据建模难模拟的耗电等目标，融入仿真优化策略。
- 在不同速度下实现电池总功耗降低24%-28%，显著提升能效。
- 方法通用易扩展，适合持续融合真实反馈改进机器人性能。
腿式运动不仅关乎移动能力，还涉及能源效率、安全性和用户体验等关键目标，对实际应用至关重要。然而，电池功耗和步态噪音等重要因素在常见仿真器中常被低估或缺失，导致当前模拟到现实的方法难以优化或忽略这些方面。人工设计的代理指标（如机械功率、脚部接触力）虽有应用，但往往依赖具体问题且不够准确。本文提出一种数据驱动的微调框架，针对难模拟的目标优化四足机器人的运动策略。该框架利用真实世界数据建模这些目标，并将学习到的模型嵌入仿真以改进策略。我们在四足机器人节电方面验证了该框架的有效性，在不同速度下实现了电池组总功耗24%-28%的显著净降低。本质上，该方法为四足运动中难以模拟的目标优化提供了一个灵活可扩展的解决方案，支持基于真实知识的持续迭代优化。项目页面 https://hard-to-sim.github.io/。
原文摘要 · Abstract (English)
Legged locomotion is not just about mobility; it also encompasses crucial objectives such as energy efficiency, safety, and user experience, which are vital for real-world applications. However, key factors such as battery power consumption and stepping noise are often inaccurately modeled or missing in common simulators, leaving these aspects poorly optimized or unaddressed by current sim-to-real methods. Hand-designed proxies, such as mechanical power and foot contact forces, have been used to address these challenges but are often problem-specific and inaccurate. In this paper, we propose a data-driven framework for fine-tuning locomotion policies, targeting these hard-to-simulate objectives. Our framework leverages real-world data to model these objectives and incorporates the learned model into simulation for policy improvement. We demonstrate the effectiveness of our framework on power saving for quadruped locomotion, achieving a significant 24-28\% net reduction in total power consumption from the battery pack at various speeds. In essence, our approach offers a versatile solution for optimizing hard-to-simulate objectives in quadruped locomotion, providing an easy-to-adapt paradigm for continual improving with real-world knowledge. Project page https://hard-to-sim.github.io/.
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