Abstract In this paper, the problem of robotic Sit-To-Stand (STS) assistance is studied. The objective is to effectively assist individuals in need to stand up from a seated position using a robot manipulator. To achieve the goal, we propose an integrated method which encompasses traditional model-based control and optimization, as well as AI-based human intention recognition. Specifically, a number of demonstrations of human-to-human STS assistance are first performed and recorded using motion capture system. On the account of the observation and recorded data, the average intended motion trajectories for the joints of lower limbs are obtained. Based on these intended motion trajectories as well as the constructed human body dynamics and control in different STS phases, an optimal nominal trajectory of the robot end-effector is generated off-line that minimizes the human joint loads while satisfying additional physical constraints. In actual STS assistance, the human who is being assisted is likely to move faster or slower from the nominal trajectories, or even sit back down. Therefore, we develop a Long Short-Term Memory (LSTM) network to estimate the ever-changing human’s intention in STS assistance, and then adjust the velocity of the robot end-effector on the basis of the predicted human intention on the nominal trajectory. Simulations and experiments are conducted, demonstrating that the proposed algorithm is indeed capable of minimizing joint load of human while following his/her intention during the course of STS motion. The algorithm can potentially be applied to future home robots that assist elderly and disabled people with daily activities.

中文翻译：

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机器人坐到站辅助的集成方法：控制框架设计和人类意图识别

摘要 本文研究了机器人Sit-To-Stand (STS) 辅助问题。目标是使用机器人操纵器有效地帮助有需要的人从坐姿站起来。为了实现这一目标，我们提出了一种综合方法，包括传统的基于模型的控制和优化，以及基于人工智能的人类意图识别。具体来说，首先使用动作捕捉系统执行和记录了一些人对人 STS 辅助的演示。根据观察和记录的数据，获得下肢关节的平均预期运动轨迹。基于这些预期的运动轨迹以及构建的不同 STS 阶段的人体动力学和控制，离线生成机器人末端执行器的最佳标称轨迹，在满足额外物理约束的同时，最大限度地减少人体关节负荷。在实际的 STS 协助中，被协助的人可能会从名义轨迹上更快或更慢地移动，甚至坐下来。因此，我们开发了一个长短期记忆 (LSTM) 网络来估计 STS 辅助中不断变化的人类意图，然后根据预测的人类意图在名义轨迹上调整机器人末端执行器的速度。进行了模拟和实验，表明所提出的算法确实能够在 STS 运动过程中遵循他/她的意图的同时最大限度地减少人体的关节负荷。