微调四足运动中难以仿真的目标：以总功率节省为例
Fine-Tuning Hard-to-Simulate Objectives for Quadruped Locomotion: A Case Study on Total Power Saving
AI总结：
针对四足运动中功耗等难以仿真目标，本文提出用真实数据学习目标模型并嵌入仿真以微调策略，实现不同速度下电池总功耗净降24-28%。
摘要：
腿足运动不仅关乎移动能力，还涵盖能效、安全性和用户体验等对真实世界应用至关重要的目标。然而，电池功耗和踏步噪声等关键因素在常用仿真器中往往建模不准确或缺失，导致这些方面在现有 sim-to-real 方法中优化不足或未被处理。机械功率和足部接触力等人工设计的代理量已被用于应对这些挑战，但通常针对具体问题且不够准确。本文提出一种数据驱动框架，用于微调运动策略，以这些难以仿真的目标为对象。该框架利用真实世界数据对这些目标建模，并将学习到的模型纳入仿真以改进策略。我们在四足运动的功率节省任务上验证了框架有效性，在不同速度下使电池组总功耗净降低显著达到24-28%。本质上，该方法为优化四足运动中难以仿真的目标提供了通用解决方案，并提供了一种易于适配、可利用真实世界知识持续改进的范式。项目页面：https://hard-to-sim.github.io/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
