用文本控制机器人动作，实现真实人形机器人稳定运动
Realizing Text-Driven Motion Generation on NAO Robot: A Reinforcement Learning-Optimized Control Pipeline
- 通过文本生成关节角度，再用强化学习优化全身运动控制
- 在真实NAO机器人上成功实现文本驱动的动作迁移
- 解决动作与机器人结构约束不匹配的问题，适合机器人动作生成研究者
针对人形机器人模仿人类动作的挑战，本文提出一种基于文本驱动的人类动作迁移方法。传统方法依赖姿态估计或动捕系统获取人类示范数据，而本工作直接从文本生成动作。为克服生成动作与机器人运动学约束之间的差异，提出基于范数-位置和旋转损失（NPR Loss）的角度信号网络，生成符合机器人结构的关节角度。这些角度作为输入，驱动基于强化学习的全身关节运动控制策略，在保证动作跟踪精度的同时维持机器人执行过程中的稳定性。实验结果表明，该方法成功将文本驱动的人类动作迁移到真实人形机器人NAO上，验证了其有效性。
原文摘要 · Abstract (English)
Human motion retargeting for humanoid robots, transferring human motion data to robots for imitation, presents significant challenges but offers considerable potential for real-world applications. Traditionally, this process relies on human demonstrations captured through pose estimation or motion capture systems. In this paper, we explore a text-driven approach to mapping human motion to humanoids. To address the inherent discrepancies between the generated motion representations and the kinematic constraints of humanoid robots, we propose an angle signal network based on norm-position and rotation loss (NPR Loss). It generates joint angles, which serve as inputs to a reinforcement learning-based whole-body joint motion control policy. The policy ensures tracking of the generated motions while maintaining the robot's stability during execution. Our experimental results demonstrate the efficacy of this approach, successfully transferring text-driven human motion to a real humanoid robot NAO.
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