ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation
This addresses the problem of enabling imitation learning for complex manipulation tasks across multiple robot platforms, though it appears incremental as it builds on existing teleoperation concepts.
The paper tackles the lack of cost-effective and user-friendly teleoperation systems for diverse robot platforms by developing ACE, a cross-platform visual-exoskeleton system that enables high-precision teleoperation for humanoid hands, arm-hands, arm-gripper, and quadruped-gripper systems.
Learning from demonstrations has shown to be an effective approach to robotic manipulation, especially with the recently collected large-scale robot data with teleoperation systems. Building an efficient teleoperation system across diverse robot platforms has become more crucial than ever. However, there is a notable lack of cost-effective and user-friendly teleoperation systems for different end-effectors, e.g., anthropomorphic robot hands and grippers, that can operate across multiple platforms. To address this issue, we develop ACE, a cross-platform visual-exoskeleton system for low-cost dexterous teleoperation. Our system utilizes a hand-facing camera to capture 3D hand poses and an exoskeleton mounted on a portable base, enabling accurate real-time capture of both finger and wrist poses. Compared to previous systems, which often require hardware customization according to different robots, our single system can generalize to humanoid hands, arm-hands, arm-gripper, and quadruped-gripper systems with high-precision teleoperation. This enables imitation learning for complex manipulation tasks on diverse platforms.