🤖 AI Summary
This work addresses the challenges of low arm–hand coordination accuracy, high feedback latency, and unstable contact interaction in dexterous teleoperation by proposing a modular bilateral teleoperation system that seamlessly integrates operator inputs with a compliant robotic arm and a dexterous hand. The system employs key techniques—including position retargeting, differential arm control, multi-scale tactile feedback, and shared autonomy—to establish a coordinated control architecture tailored for real-world contact-rich environments. It further elucidates design principles concerning cross-embodiment mismatch, tactile feedback granularity, and shared control strategies. Experimental results demonstrate that the system achieves stable and efficient arm–hand coordination in complex dexterous manipulation tasks and provides a high-quality data acquisition platform for imitation learning.
📝 Abstract
Dexterous teleoperation requires precise arm-hand coordination, low-latency feedback, and robust interaction in real-world contact-rich environments. This paper presents a modular bilateral teleoperation framework that integrates operator-side input interfaces with a robot-side dexterous hand and compliant robotic arm in a unified control architecture. The system supports position-based hand retargeting, differential arm control, multi-scale haptic feedback, and shared control for stable manipulation. We validate the framework through a real-world dexterous manipulation task, highlighting coordinated arm-hand control and contact-aware interaction. Beyond feasibility, we identify key design insights related to cross-embodiment mismatch, haptic feedback granularity, and shared control. The proposed platform provides a practical teleoperation system and a foundation for collecting high-quality demonstrations for future learning-from-demonstration research.