I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning
<|reference_start|>I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning: 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.<|reference_end|>
@article{yan2024i-ctrl:,
    title={I-CTRL: Imitation to Control Humanoid Robots Through Constrained
  Reinforcement Learning},
    author={Yashuai Yan, Esteve Valls Mascaro, Tobias Egle, Dongheui Lee},
    journal={arXiv preprint arXiv:2405.08726},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08726},
    primaryClass={cs.RO cs.AI}
}