This paper addresses the critical need for refining robot motions that, despite achieving a high visual similarity through human-to-humanoid retargeting methods, fall short of practical execution in the physical realm. Existing techniques in the graphics community often prioritize visual fidelity over physics-based feasibility, posing a significant challenge for deploying bipedal systems in practical applications. Our research introduces a constrained reinforcement learning algorithm to produce physics-based high-quality motion imitation onto legged humanoid robots that enhance motion resemblance while successfully following the reference human trajectory. We name our framework: I-CTRL. By reformulating the motion imitation problem as a constrained refinement over non-physics-based retargeted motions, our framework excels in motion imitation with simple and unique rewards that generalize across four robots. Moreover, our framework can follow large-scale motion datasets with a unique RL agent. The proposed approach signifies a crucial step forward in advancing the control of bipedal robots, emphasizing the importance of aligning visual and physical realism for successful motion imitation.

翻译：本文针对通过人体到类人机器人重定向方法虽获得高视觉相似度，但在实际物理执行中存在不足的机器人运动优化这一关键需求展开研究。图形学领域的现有技术往往优先考虑视觉保真度而非基于物理的可行性，这对双足系统在实际应用中的部署构成了重大挑战。本研究提出一种约束强化学习算法，能够为腿式类人机器人生成基于物理的高质量运动模仿，在成功跟随参考人体轨迹的同时增强运动相似性。我们将该框架命名为I-CTRL。通过将运动模仿问题重构为基于非物理重定向运动的约束优化过程，本框架采用简单且独特的奖励函数即可在四种机器人上实现卓越的运动模仿性能。此外，该框架能够利用单一强化学习智能体跟随大规模运动数据集。所提出的方法标志着双足机器人控制领域的关键进展，强调了视觉真实感与物理真实感对齐对成功实现运动模仿的重要性。