用质心角速度奖励+真实电机建模，让单腿跳跃器首次完成翻跟头。
Learning Impact-Rich Rotational Maneuvers via Centroidal Velocity Rewards and Sim-to-Real Techniques: A One-Leg Hopper Flip Case Study
- 用质心角速度奖励替代传统关节奖励，驱动全身旋转。
- 在硬件上实现完整前空翻，成功解决仿真到现实的迁移难题。
- 适合做高动态机器人控制、仿生运动规划的研究者参考。
动态旋转动作如前空翻，涉及巨大的角动量和强烈的冲击力，对强化学习和仿真到现实的迁移带来重大挑战。本文提出一种通用框架，通过基于质心速度的奖励和考虑执行器特性的仿真到现实技术，学习并部署具有强冲击、高旋转的动作。我们发现传统基于连杆层级的奖励无法激发真正的全身旋转，因此引入质心角速度奖励，准确捕捉系统整体旋转动力学。为在极端条件下弥合仿真与现实的差距，我们建模电机工作区域（MOR），并应用传动负载正则化，确保扭矩指令真实且机械鲁棒。以单腿跳跃器前空翻为例，首次在硬件上实现完整前空翻。结果表明，融入质心动力学与执行器约束，对可靠执行高度动态运动至关重要。补充视频见：https://youtu.be/atMAVI4s1RY
原文摘要 · Abstract (English)
Dynamic rotational maneuvers, such as front flips, inherently involve large angular momentum generation and intense impact forces, presenting major challenges for reinforcement learning and sim-to-real transfer. In this work, we propose a general framework for learning and deploying impact-rich, rotation-intensive behaviors through centroidal velocity-based rewards and actuator-aware sim-to-real techniques. We identify that conventional link-level reward formulations fail to induce true whole-body rotation and introduce a centroidal angular velocity reward that accurately captures system-wide rotational dynamics. To bridge the sim-to-real gap under extreme conditions, we model motor operating regions (MOR) and apply transmission load regularization to ensure realistic torque commands and mechanical robustness. Using the one-leg hopper front flip as a representative case study, we demonstrate the first successful hardware realization of a full front flip. Our results highlight that incorporating centroidal dynamics and actuator constraints is critical for reliably executing highly dynamic motions. A supplementary video is available at: https://youtu.be/atMAVI4s1RY
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。