Human-Like Whole-Body Motion Control for Humanoid Robot with Integrated Virtual Waist Joints
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摘要: 复杂运动的稳定性与社会环境的适应性是人形机器人实现自然交互与任务执行的核心能力, 而高质量、高协调的仿人运动是保障其高效稳定运行的基础. 腰部自由度作为上下肢协调控制的关键枢纽, 对整体动态稳定性与动作流畅性具有显著影响. 然而, 现有研究在腰部自由度建模与控制方面关注不足, 导致机器人在仿人行走中易出现姿态畸变甚至运动失稳. 为此, 提出一种虚拟腰部自由度引导的多关节主动补偿控制框架. 该框架首先利用动作捕捉系统采集人体多维关节点数据, 并通过关节空间映射实现运动风格的高保真迁移. 随后, 针对人体腰部与机器人结构间的自由度差异, 构建融合俯仰与滚转虚拟关节的上下肢动力学模型, 提出多关节主动补偿机制以实现对虚拟腰部自由度的协同补偿. 进一步地, 将虚拟关节引入特权观测空间, 构建对抗式动作先验风格奖励与任务执行奖励的可变融合策略, 从而实现全身协调的仿人运动控制. 实验基于Isaac Gym训练平台与MuJoCo仿真环境, 在多速率运动指令 (0.2–1.6 m/s) 下开展测试, 结果表明所提方法能显著提升人形机器人的仿人运动自然度与泛化能力, 相较现有腰部受限方法具有更优性能.Abstract: Stable and adaptive motion is fundamental for humanoid robots to achieve natural interaction and efficient task execution, where high-quality and coordinated human-like motion serves as the foundation for stable performance. As the key hub for upper–lower limb coordination, the waist plays a critical role in maintaining overall dynamic stability and motion fluency. However, limited attention to waist degree-of-freedom modeling and control in existing studies often leads to posture distortion or instability during human-like walking. To address this issue, this paper proposes a virtual waist DOF–guided multi-joint active compensation (VW-MAC) control framework. The framework first employs a motion capture system to acquire multi-dimensional human joint data, which are mapped into the humanoid robot's joint space to achieve high-fidelity transfer of motion style. Then, considering the degree-of-freedom discrepancy between the human waist and the robot structure, a coupled upper–lower limb dynamic model incorporating virtual pitch and roll waist joints is constructed, and a multi-joint active compensation mechanism is developed to realize coordinated compensation for the virtual waist DOFs. Furthermore, the virtual joints are incorporated into the privileged observation space, and a variable fusion strategy combining adversarial motion prior style rewards and task rewards is designed to achieve whole-body coordinated human-like motion control. Experiments conducted in the Isaac Gym training platform and MuJoCo simulation environment demonstrate that the proposed method significantly improves the naturalness and generalization of human-like motion under various walking speeds (0.2–1.6 m/s), outperforming existing waist-constrained approaches.
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图 4 离散速度指令下机器人质心、髋关节及踝关节空间轨迹
Fig. 4 Spatial trajectories of the robot＇s CoM, hip, and ankle under discrete velocity commands
图 5 速度指令1.5 m/s下机器人行走关键帧切片
Fig. 5 Keyframe slices of humanoid robot walking at 1.5 m/s velocity command
图 6 速度指令0.5 m/s下人形机器人行走关键帧切片
Fig. 6 Keyframe slices of humanoid robot walking at 0.5 m/s velocity command
表 1 AMP[31]与AMP+虚拟关节算法在人形机器人行走动作中的左右腿对称性对比
Table 1 Comparison of left-right leg symmetry between AMP[31] and VW-MAC during humanoid robot walking
速度指令 算法 髋关节屈伸↓ 髋关节旋转↓ 髋关节外展↓ 膝关节屈伸↓ 踝关节屈伸↓ 踝关节旋转↓ 有变融合机制 AMP 0.029943 0.038185 0.038442 0.167267 0.049829 0.064260 VW-MAC 0.008752 0.025621 0.025128 0.028924 0.016321 0.019843 cmd=1.0 m/s AMP 0.006264 0.015836 0.024013 0.232104 0.248462 0.052555 VW-MAC 0.005491 0.014215 0.011845 0.119843 0.135621 0.048926 cmd=1.5 m/s AMP 0.021222 0.029997 0.034885 0.241722 0.186154 0.067396 VW-MAC 0.019843 0.027621 0.032128 0.128924 0.175321 0.062843 无变融合机制 AMP 0.013942 0.045621 0.048166 0.188946 0.065191 0.072661 VW-MAC 0.011667 0.041237 0.042465 0.178993 0.058221 0.068186 cmd=1.0 m/s AMP 0.009331 0.022136 0.034272 0.262222 0.279343 0.074722 VW-MAC 0.008541 0.026443 0.031635 0.249911 0.266621 0.068837 cmd=1.5 m/s AMP 0.029396 0.039741 0.052112 0.263180 0.211401 0.092015 VW-MAC 0.026962 0.035516 0.047535 0.257881 0.203851 0.082095 
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