通过负载感知：负载交互下的柔顺四足运动

中文摘要 AI 辅助

四足机器人越来越需要携带物体在人类环境中移动。但当一个人直接与负载交互而不是与机器人交互时，会发生什么？如果负载不受约束，机器人必须区分有意的外部交互与普通的负载运动，同时保持负载平衡并维持稳定的运动。四足机器人如何仅利用机载测量来推断并柔顺地响应此类交互？在这项工作中，我们开发了一个力感知的运动框架，将负载交互视为塑造机器人-负载组合系统运动的命令。我们的方法将力感知运动的学习与不受约束负载上的力估计分开。我们将一个柔顺的负载携带策略与一个因果力估计器相结合，通过估计器在环数据聚合和微调进行训练，以从机载机器人测量中预测交互。我们的仿真和真实世界实验表明，所得到的控制器能够维持稳定的负载携带运动，柔顺地屈服于外部交互，并利用推断的力来支持人类引导的机器人轨迹变化。

英文摘要

Quadruped robots are increasingly expected to carry objects while moving through human environments. But what happens when a person interacts directly with the payload rather than with the robot? If the payload is unrestrained, the robot must distinguish intentional external interactions from ordinary payload motion, while still keeping the load balanced and maintaining stable locomotion. How can a quadruped infer and compliantly respond to such interactions using only onboard measurements? In this work, we develop a force-aware locomotion framework that treats payload interactions as commands that shape the motion of the combined robot-payload system. Our approach separates the learning of force-aware locomotion and force estimation on an unrestrained payload. We combine a compliant load-carrying policy with a causal force estimator, trained through estimator-in-the-loop data aggregation and finetuning, to predict interactions from onboard robot measurements. Our simulations and real-world experiments show that the resulting controller can maintain stable payload-carrying locomotion, yield compliantly to external interactions, and use the inferred force to support human-guided changes in the robot's trajectory.

发表机构

• Fujitsu Research of America（富士通美国研究院）

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