| Autor: |
Yan, Yashuai, Mascaro, Esteve Valls, Egle, Tobias, Lee, Dongheui |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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