The ability to accomplish manipulation and locomotion tasks in the presence of significant time-varying external loads is a remarkable skill of humans that has yet to be replicated convincingly by humanoid robots. Such an ability will be a key requirement in the environments we envision deploying our robots: dull, dirty, and dangerous. External loads constitute a large model bias, which is typically unaccounted for. In this work, we enable our humanoid robot to engage in loco-manipulation tasks in the presence of significant model bias due to external loads. We propose an online estimation and control framework involving the combination of a physically consistent extended Kalman filter for inertial parameter estimation coupled to a whole-body controller. We showcase our results both in simulation and in hardware, where weights are mounted on Nadia's wrist links as a proxy for engaging in tasks where large external loads are applied to the robot.

翻译：在承受显著时变外部载荷时完成操作与移动任务，是人类的一项卓越技能，而人形机器人尚未能令人信服地复现这一能力。这种能力将是我们设想部署机器人的环境——枯燥、肮脏、危险环境中的关键需求。外部载荷构成一种通常未被考虑的大幅模型偏差。在本研究中，我们使机器人能够在因外部载荷导致显著模型偏差的情况下执行全身操作与移动任务。我们提出了一种结合物理一致扩展卡尔曼滤波进行惯性参数估计的在线估计与控制框架，并将其与全身控制器相结合。我们通过仿真和硬件实验展示了结果，实验中在Nadia机器人腕部连杆上安装配重，以模拟机器人承受大外部载荷时的任务场景。