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AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System

About

Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment, which scales poorly as the pool of the robot models expands and the variety of the operating environment increases. In this paper, we propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system. Although being designed to provide great flexibility to the choice of simulators and real hardware, our system can still achieve great performance. For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to better imitation learning performance, compared with a previous system that is particularly designed for that simulator. Project page: https://yzqin.github.io/anyteleop/.

Yuzhe Qin, Wei Yang, Binghao Huang, Karl Van Wyk, Hao Su, Xiaolong Wang, Yu-Wei Chao, Dieter Fox• 2023

Related benchmarks

TaskDatasetResultRank
USB InsertionUSB Insertion
Success Rate93.3
19
Finger Motion RetargetingSign Dataset
Thumb Error (rad)0.2046
4
Spaghetti ProbingSpaghetti Probing
Success Rate57.5
4
Arm and Finger Motion RetargetingCSL-Daily
MPJPE (m)0.9381
4
Arm Motion RetargetingSign Dataset
MPJPE (m)0.1021
4
Key InsertionKey Insertion
Success Rate63.2
4
Hand RetargetingWuji hand platform (simulation)
Mean Latency (ms)15.59
3
Turning-angle trackingReal-world ArUco Reference (test)
RMSE (°)23.2
2
Turning-angle trackingReal-world Human Intent Reference (test)
RMSE (°)40.3
2
Turning-angle trackingSimulation
RMSE (°)27.7
2
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