DexTele：一种基于运动重定向和自适应力控制的双臂灵巧遥操作系统
DexTele: A Dual-Arm Dexterous Teleoperation System Based on Motion Retargeting and Adaptive Force Control
AI总结：
研究针对双臂灵巧遥操作中运动重定向和抓取的挑战，提出DexTele系统，通过视觉运动重定向模块和自适应抓取模块，结合运动图编码器、视觉语言模型等技术，实现多平台精确运动重定向与柔顺抓取。
摘要：
在双臂灵巧遥操作中，运动重定向的跨平台泛化和抓取的交互性至关重要。然而，机器人架构的异质性和抓取对象的多样性对实现精确的运动重定向和柔顺抓取提出了重大挑战。为应对这些挑战，提出了一种基于运动重定向和自适应力控制的双臂灵巧遥操作系统（DexTele）。首先，设计了基于视觉的运动重定向模块，通过运动图编码器和潜在优化实现跨平台运动重定向。其次，设计了自适应抓取模块，结合视觉语言模型和模型预测控制实现柔顺抓取。最后，实验表明DexTele能在多个机器人平台上实现精确运动重定向和柔顺抓取。
英文摘要：
In dual-arm dexterous teleoperation, cross-platform generalization of motion retargeting and interactivity of grasping are crucial. However, the heterogeneity of robotic architectures and the wide variety of grasping objects pose significant challenges to achieving precise motion retargeting and compliant grasping in dual-arm dexterous teleoperation. To address these challenges, a dual-arm dexterous teleoperation system (DexTele) is proposed based on motion retargeting and adaptive force control. First, a vision-based motion retargeting module is designed to generate preliminary robot motions from human images. In this module, a motion-graph encoder and latent optimization are proposed for precise and convenient cross-platform motion retargeting. Second, an adaptive grasping module is designed to achieve compliant grasping. This module combines a vision-language model (VLM) with model predictive control (MPC), allowing the system to predict the required grasping force for a target object and perform gradient-based online optimization. Finally, extensive experiments demonstrate that the DexTele achieves precise motion retargeting and compliant grasping with generalization across multiple robot platforms.
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