We envision a humanoid robot to serve as a source of additional motion-support forces in assistance for frail persons. In this context, we present a control strategy for a humanoid to adaptively regulate its assistive force contribution. First, we identify a human model torque control for optimal execution of a priori known motion task from sample recordings of this task performed by a healthy individual. We utilize the identified model in the proposed position discrepancy based observer of the human torque contribution, the unknown and unmeasurable variable. We propose an experience-based human contribution model learning strategy that allows to improve the human contribution estimate from trial-to-trial. The target assistive torque contribution is then calculated as the difference between the optimal torque required for the motion task and the estimated human contribution. The target assistive torque is integrated into a multi-robot quadratic programming task-space controller to compute the desired interaction force required for the robot to supply the necessary assistive torque for the human model. We use the non-optimal recordings of the human motion task to emulate human frailty and apply our adaptive force control strategy to demonstrate the results of a humanoid successfully assisting the simulated human model to restore the optimal motion task performance.

中文翻译：

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用于人形对人类援助的自适应任务太空部队控制


我们设想一个人形机器人作为额外运动支持力的来源来帮助体弱的人。在这种背景下，我们提出了一种人形机器人的控制策略，以自适应地调节其辅助力贡献。首先，我们从健康个体执行的先验已知运动任务的样本记录中确定用于最佳执行该任务的人体模型扭矩控制。我们在所提出的基于位置差异的人体扭矩贡献观测器中利用所识别的模型，该观测器是未知且不可测量的变量。我们提出了一种基于经验的人类贡献模型学习策略，可以改进每次试验的人类贡献估计。然后，目标辅助扭矩贡献被计算为运动任务所需的最佳扭矩与估计的人类贡献之间的差。目标辅助扭矩被集成到多机器人二次编程任务空间控制器中，以计算机器人为人体模型提供必要的辅助扭矩所需的所需交互力。我们使用人体运动任务的非最佳记录来模拟人类的脆弱性，并应用我们的自适应力控制策略来演示类人机器人成功协助模拟人体模型恢复最佳运动任务性能的结果。