CompliantWBC：通过力潜在估计和残差阻抗目标实现重型人形机器人的全身柔顺控制
CompliantWBC: Whole-Body Compliance for Heavy Humanoids via Force Latent Estimation and Residual Impedance Targets
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中文总结 AI 辅助
CompliantWBC通过强化学习、残差阻抗策略和力来源采样，实现重型人形机器人全身柔顺控制，仿真偏差2.58cm，并已在真实机器人上验证。
中文摘要 AI 辅助
全身柔顺控制对于在高载荷下于人类中心环境中部署重型人形机器人至关重要。先前大多数基于力的学习型流水线专注于末端执行器阻力、每连杆上身弹簧或末端执行器刚度调制，而忽略了重型平台上涉及下身参与的任意部位扰动问题。我们通过CompliantWBC弥补了这一空白，其包括：（1）一个基于强化学习训练的基础策略，用于最大化柔顺保真度奖励，并由多部位全身阻抗参考控制器引导，将经典笛卡尔阻抗扩展到任意受控连杆；（2）一个有界残差策略，在冻结的基础策略上编辑每连杆阻抗平衡点，修正由梯度屏障后共同训练的力编码器提供的粗略但结构化的力估计；（3）一个Phong加权力来源采样器，带有轴解耦骨盆锚点，通过两个可解释参数诱导包含下身的柔顺课程训练。我们在仿真中将CompliantWBC与柔顺和刚性基线进行比较，实现了最佳柔顺保真度，即与分析解的偏差为2.58厘米，并在真实重型人形机器人上进行了演示，涵盖静态/动态力反应、擦板、载荷下蹲和协作载荷运输。项目网站：此https URL
英文摘要
Whole-body compliant control is essential for deploying heavy humanoids under high payload in human-centric environments. Most prior force-aware learning-based pipelines focus on end-effector resistance, per-link upper-body springs, or end-effector stiffness modulation, leaving arbitrary-site perturbations on heavy platforms with lower-body engagement largely unaddressed. We close this gap with CompliantWBC comprising: (1) A base policy trained with RL to maximize compliance-fidelity reward, guided by a multi-site whole-body impedance reference controller, extending classical Cartesian impedance to any controlled link; (2) A bounded residual policy that edits the per-link impedance equilibrium over a frozen base, correcting the coarse but structured wrench estimate supplied by a force encoder co-trained behind a gradient barrier; (3) A Phong-weighted force-origin sampler with an axis-decoupled pelvis anchor induces lower-body-inclusive compliance curriculum training via two interpretable parameters. We evaluate CompliantWBC in simulation against both compliant and stiff baselines, achieving best compliant fidelity of 2.58cm deviation from analytical solutions, and demonstrate it on a real heavy humanoid across static/dynamic force reaction, board wiping, squat under payload, and cooperative payload transport. Project website: https://dotandung.github.io/compliantwbc/
发表机构
- National University of Singapore（新加坡国立大学）
- VinRobotics（Vin机器人公司）
- VinUniversity（文森大学）
- TU Darmstadt（达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。
