对比不同操作设备与控制器组合，提升机器人远程操控的灵活性。
Empowering Robot Teleoperation: Exploring the Synergies Between Devices and Manipulator Controllers in a Comparative Study
- 测试三种控制器：位置逆解、力矩逆动力学、优化式柔顺控制
- 设备与控制器匹配度影响操控效果，特定组合表现更优
- 适合机器人远程操控系统设计者参考
机器人学习赋予系统类人智能，通过经验自主获取并适应技能，增强在多环境中的灵活性与适应性。为在具身智能中实现大语言模型（LLMs）类似能力，训练基础模型所需数据的质量至关重要。本研究聚焦于使用远程操控设备采集操作任务数据。不同设备与对应控制器策略（包括基于位置的逆运动学控制、基于力矩的逆动力学控制、优化式柔顺控制）搭配时表现出显著差异。实验结果分析表明，远程操控设备与控制器之间的协同关系对真实任务表现具有关键影响。
原文摘要 · Abstract (English)
Robot learning empowers the robot system with human brain-like intelligence to autonomously acquire and adapt skills through experience, enhancing flexibility and adaptability in various environments. Aimed at achieving a similar level of capability in large language models (LLMs) for embodied intelligence, data quality plays a crucial role in training a foundational model with diverse robot skills. In this study, we investigate the collection of data for manipulation tasks using teleoperation devices. Different devices yield varying effects when paired with corresponding controller strategies, including position-based inverse kinematic (IK) control, torque-based inverse dynamic (ID) control, and optimization-based compliant control. Analysis of experimental results suggests the importance of the relationship between teleoperation devices and controllers for real tasks.
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