面向物理人机协作的自适应刚度控制的生成式动作块采样
Generative Action-Chunk Sampling for Adaptive Stiffness Control in Physical Human-Robot Collaboration
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中文总结 AI 辅助
针对物理人机协作的协助与柔顺性平衡问题，提出基于生成式动作块采样的自适应刚度框架，在四方向协作搬运任务中平均成功率达0.95，优于固定刚度组与确定性基线。
中文摘要 AI 辅助
物理人机协作要求机器人在人类意图明确时提供协助，而在未来多种运动可能性下保持柔顺性。我们提出一种基于生成式动作块采样的自适应刚度框架。该策略以RGB图像和外部关节力矩估计为条件，从观测条件先验中采样多个未来动作块。利用采样动作块间的差异持续调整关节刚度和阻尼：差异越大，机器人越柔顺以方便人类引导；差异越小，提供的协助越稳固。在含四个可能方向的真实协作搬运任务中，所提方法的平均成功率达0.95，而固定刚度 ablation 组为0.83，确定性基线为0.69。在接近方向确定时，采样动作块间的差异增大，控制器相应降低刚度。这些结果表明，生成式策略采样的动作间差异可作为在线控制信号，用于平衡物理人机交互中的协助与柔顺性。
英文摘要
Physical human-robot collaboration requires a robot to provide assistance when human intention is clear while remaining compliant when several future motions are plausible. We present an adaptive stiffness framework based on generative action-chunk sampling. Conditioned on an RGB image and external joint-torque estimates, the policy samples multiple latent variables from an observation-conditioned prior and decodes them into future action chunks. Variation among the sampled action chunks is used to continuously adapt joint stiffness and damping. Greater variation makes the robot more compliant to facilitate human guidance, whereas lower variation provides firmer assistance. In a real-world collaborative transport task with four possible directions, the proposed method achieved an average success rate of 0.95, compared with 0.83 for a fixed-stiffness ablation and 0.69 for a deterministic baseline. Near direction determination, variation among the sampled action chunks increased, and the controller reduced stiffness accordingly. These results suggest that variation among actions sampled by a generative policy can serve as an online control signal for balancing assistance and compliance in physical human-robot interaction.
发表机构
- School of Engineering and Design, Graduate School of Science and Technology, Keio University（庆应义塾大学 理工学研究科 设计工程学院）
机构由 AI 辅助整理，请以论文原文为准。
