A Combined Learning and Optimization Framework to Transfer Human Whole-body Loco-manipulation Skills to Mobile Manipulators
<|reference_start|>A Combined Learning and Optimization Framework to Transfer Human Whole-body Loco-manipulation Skills to Mobile Manipulators: 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.<|reference_end|>
@article{zhao2024a,
    title={A Combined Learning and Optimization Framework to Transfer Human
  Whole-body Loco-manipulation Skills to Mobile Manipulators},
    author={Jianzhuang Zhao (1,2), Francesco Tassi (1), Yanlong Huang (3), Elena
  De Momi (2), and Arash Ajoudani (1) ((1) Human-Robot Interfaces and
  Interaction Lab, Istituto Italiano di Tecnologia, Genoa, Italy, (2) Dept. of
  Electronics, Information, and Bioengineering, Politecnico di Milano, Italy,
  (3) School of Computing, University of Leeds, Leeds, UK)},
    journal={arXiv preprint arXiv:2402.13915},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13915},
    primaryClass={cs.RO}
}