TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
<|reference_start|>TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation: A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available and easy-to-use teleoperation interfaces. In this work, we demonstrate TeleMoMa, a general and modular interface for whole-body teleoperation of mobile manipulators. TeleMoMa unifies multiple human interfaces including RGB and depth cameras, virtual reality controllers, keyboard, joysticks, etc., and any combination thereof. In its more accessible version, TeleMoMa works using simply vision (e.g., an RGB-D camera), lowering the entry bar for humans to provide mobile manipulation demonstrations. We demonstrate the versatility of TeleMoMa by teleoperating several existing mobile manipulators - PAL Tiago++, Toyota HSR, and Fetch - in simulation and the real world. We demonstrate the quality of the demonstrations collected with TeleMoMa by training imitation learning policies for mobile manipulation tasks involving synchronized whole-body motion. Finally, we also show that TeleMoMa's teleoperation channel enables teleoperation on site, looking at the robot, or remote, sending commands and observations through a computer network, and perform user studies to evaluate how easy it is for novice users to learn to collect demonstrations with different combinations of human interfaces enabled by our system. We hope TeleMoMa becomes a helpful tool for the community enabling researchers to collect whole-body mobile manipulation demonstrations. For more information and video results, https://robin-lab.cs.utexas.edu/telemoma-web.<|reference_end|>
@article{dass2024telemoma:,
    title={TeleMoMa: A Modular and Versatile Teleoperation System for Mobile
  Manipulation},
    author={Shivin Dass, Wensi Ai, Yuqian Jiang, Samik Singh, Jiaheng Hu, Ruohan
  Zhang, Peter Stone, Ben Abbatematteo, Roberto Mart'in-Mart'in},
    journal={arXiv preprint arXiv:2403.07869},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07869},
    primaryClass={cs.RO cs.AI cs.LG}
}