Contact Compliance Visuo-Proprioceptive Policy for Contact-Rich Manipulation with Cost-Efficient Haptic Hand-Arm Teleoperation System
<|reference_start|>Contact Compliance Visuo-Proprioceptive Policy for Contact-Rich Manipulation with Cost-Efficient Haptic Hand-Arm Teleoperation System: Learning robot manipulation skills in real-world environments is extremely challenging. Robots learning manipulation skills in real-world environments is extremely challenging. Recent research on imitation learning and visuomotor policies has significantly enhanced the ability of robots to perform manipulation tasks. In this paper, we propose Admit Policy, a visuo-proprioceptive imitation learning framework with force compliance, designed to reduce contact force fluctuations during robot execution of contact-rich manipulation tasks. This framework also includes a hand-arm teleoperation system with vibrotactile feedback for efficient data collection. Our framework utilizes RGB images, robot joint positions, and contact forces as observations and leverages a consistency-constrained teacher-student probabilistic diffusion model to generate future trajectories for end-effector positions and contact forces. An admittance model is then employed to track these trajectories, enabling effective force-position control across various tasks.We validated our framework on five challenging contact-rich manipulation tasks. Among these tasks, while improving success rates, our approach most significantly reduced the mean contact force required to complete the tasks by up to 53.92% and decreased the standard deviation of contact force fluctuations by 76.51% compared to imitation learning algorithms without dynamic contact force prediction and tracking.<|reference_end|>
@article{zhou2024admittance,
    title={Admittance Visuomotor Policy Learning for General-Purpose Contact-Rich
  Manipulations},
    author={Bo Zhou, Ruixuan Jiao, Yi Li, Xiaogang Yuan, Fang Fang, and Shihua Li},
    journal={arXiv preprint arXiv:2409.14440},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14440},
    primaryClass={cs.RO}
}