系统梳理机器人高接触任务模仿学习的研究进展
A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
- 分析演示数据采集方法与多模态感知技术
- 总结基础模型提升复杂接触任务性能的关键作用
- 适合关注机器人操作与模仿学习的科研人员
本文全面综述了机器人高接触任务中模仿学习的研究趋势。这类任务需与环境进行复杂物理交互，因非线性动力学和对微小位置偏差的高度敏感性，成为机器人领域的核心挑战。论文考察了示范数据采集方法，包括教学方式与关键感官模态，以捕捉细微的交互动态。随后分析模仿学习方法在高接触操作中的应用。近年来，多模态学习与基础模型的进展显著提升了工业、家庭及医疗领域复杂接触任务的表现。通过系统化整理现有研究并识别关键挑战，本综述为未来高接触机器人操作的发展奠定了基础。
原文摘要 · Abstract (English)
This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze imitation learning approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.
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