Bimanual Dexterity for Complex Tasks
This addresses the problem of data collection for robot learning by providing a more accessible and effective teleoperation system for researchers and practitioners, though it is incremental as it builds on existing teleoperation methods.
The authors tackled the challenge of collecting expert human teleoperation data for generalist robot policies by introducing Bidex, a low-cost, portable bimanual teleoperation system with over 50 DoF, which produced better quality data for complex tasks at a faster rate compared to Vision Pro and SteamVR systems.
To train generalist robot policies, machine learning methods often require a substantial amount of expert human teleoperation data. An ideal robot for humans collecting data is one that closely mimics them: bimanual arms and dexterous hands. However, creating such a bimanual teleoperation system with over 50 DoF is a significant challenge. To address this, we introduce Bidex, an extremely dexterous, low-cost, low-latency and portable bimanual dexterous teleoperation system which relies on motion capture gloves and teacher arms. We compare Bidex to a Vision Pro teleoperation system and a SteamVR system and find Bidex to produce better quality data for more complex tasks at a faster rate. Additionally, we show Bidex operating a mobile bimanual robot for in the wild tasks. The robot hands (5k USD) and teleoperation system (7k USD) is readily reproducible and can be used on many robot arms including two xArms (16k USD). Website at https://bidex-teleop.github.io/