A Combined Learning and Optimization Framework to Transfer Human Whole-body Loco-manipulation Skills to Mobile Manipulators
CoRR(2024)
摘要
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.
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