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Telemoma: A Modular and Versatile Teleoperation System for Mobile Manipulation.
Shivin Dass, Wensi Ai, Yuqian
Jiang, Samik Singh, Jiaheng Hu,
Ruohan Zhang, Peter Stone,
Ben Abbatematteo, and Roberto Martin-Martin.
In ICRA Workshop on Mobile Manipulation and Embodied Intelligence,
May 2024.
A critical bottleneck limiting imitation learning in robotics is the lack ofdata. This problem is more severe in mobile manipulation, where collectingdemonstrations is harder than in stationary manipulation due to the lack ofavailable and easy-to-use teleoperation interfaces. In this work, we demonstrateTeleMoMa, a general and modular interface for whole-body teleoperation of mobilemanipulators. TeleMoMa unifies multiple human interfaces including RGB and depthcameras, virtual reality controllers, keyboard, joysticks, etc., and anycombination thereof. In its more accessible version, TeleMoMa works using simplyvision (e.g., an RGB-D camera), lowering the entry bar for humans to providemobile manipulation demonstrations. We demonstrate the versatility of TeleMoMa byteleoperating several existing mobile manipulators - PAL Tiago++, Toyota HSR, andFetch - in simulation and the real world. We demonstrate the quality of thedemonstrations collected with TeleMoMa by training imitation learning policiesfor 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 acomputer network, and perform user studies to evaluate how easy it is for noviceusers to learn to collect demonstrations with different combinations of humaninterfaces enabled by our system. We hope TeleMoMa becomes a helpful tool for thecommunity enabling researchers to collect whole-body mobile manipulationdemonstrations. For more information and video results,https://robin-lab.cs.utexas.edu/telemoma-web/.
@InProceedings{dass_telemoma2024,
author = {Shivin Dass and Wensi Ai and Yuqian Jiang and Samik Singh and Jiaheng Hu and Ruohan Zhang and Peter Stone and Ben Abbatematteo and Roberto Martin-Martin},
title = {Telemoma: A Modular and Versatile Teleoperation System for Mobile Manipulation},
booktitle = {ICRA Workshop on Mobile Manipulation and Embodied Intelligence},
year = {2024},
month = {May},
location = {Yokohama, Japan},
abstract = {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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