Humans' ability to smoothly switch between locomotion and manipulation is a remarkable feature of sensorimotor coordination. Leaning and replication of such human-like strategies can lead to the development of more sophisticated robots capable of performing complex whole-body tasks in real-world environments. To this end, this paper proposes a combined learning and optimization framework for transferring human's loco-manipulation soft-switching skills to mobile manipulators. The methodology departs from data collection of human demonstrations for a locomotion-integrated manipulation task through a vision system. Next, the wrist and pelvis motions are mapped to mobile manipulators' End-Effector (EE) and mobile base. A kernelized movement primitive algorithm learns the wrist and pelvis trajectories and generalizes to new desired points according to task requirements. Next, the reference trajectories are sent to a hierarchical quadratic programming controller, where the EE and the mobile base reference trajectories are provided as the first and second priority tasks, generating the feasible and optimal joint level commands. A locomotion-integrated pick-and-place task is executed to validate the proposed approach. After a human demonstrates the task, a mobile manipulator executes the task with the same and new settings, grasping a bottle at non-zero velocity. The results showed that the proposed approach successfully transfers the human loco-manipulation skills to mobile manipulators, even with different geometry.

翻译：人类在移动与操作之间平滑切换的能力是感觉运动协调的一个显著特征。学习并复制此类类人策略，有助于开发能在真实环境中执行复杂全身任务的更精密机器人。为此，本文提出一种结合学习与优化的框架，用于将人体移动操作柔顺切换技能迁移至移动机械臂。该方法首先通过视觉系统采集人体在移动-操作一体化任务中的演示数据，随后将手腕和骨盆运动分别映射至移动机械臂的末端执行器和移动基座。采用核化运动基元算法学习手腕和骨盆轨迹，并根据任务需求泛化至新的目标点。接着，参考轨迹被输入至分层二次规划控制器，其中末端执行器和移动基座的参考轨迹分别作为第一优先级和第二优先级任务，生成可行且最优的关节级指令。通过执行集移动与操作于一体的抓取-放置任务验证所提方法：在人体演示任务后，移动机械臂以相同的参数设置及新的参数设置执行任务，并在非零速度下抓取水杯。结果表明，该方法即使面对不同几何构型，也能成功将人体移动操作技能迁移至移动机械臂。