TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation
arxiv(2024)
摘要
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.
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