交互模仿学习在灵巧机械手操作中的应用：挑战与展望——综述
Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey
AI总结：
本文综述了交互模仿学习在灵巧机械手操作中的挑战与前景，探讨了该方法在提升机器人灵巧操作能力方面的潜力与改进方向。
摘要：
灵巧操作是人形机器人中至关重要却高度复杂的挑战，需要精确、适应性强且样本效率高的学习方法。由于人形机器人通常设计用于在以人为中心的环境中操作和与日常物品互动，掌握灵巧操作对于实际应用至关重要。传统方法，如强化学习和模仿学习，已取得显著进展，但它们在现实世界灵巧操作中面临独特挑战，包括高维控制、有限的训练数据和协变量偏移。本文综述了这些挑战，并回顾了现有基于学习的方法用于现实世界灵巧操作，涵盖模仿学习、强化学习和混合方法。一个有前途但尚不充分探索的方向是交互模仿学习，其中人类反馈在训练过程中主动优化机器人行为。尽管交互模仿学习在各种机器人任务中已取得成功，但其在灵巧操作中的应用仍有限。为解决这一差距，本文考察了当前交互模仿学习技术在其他机器人任务中的应用，并讨论这些方法如何适应以增强灵巧操作。通过综合最前沿的研究，本文突出了关键挑战，指出了当前方法的不足，并概述了利用交互模仿学习改进灵巧机器人技能的潜在方向。
英文摘要：
Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to operate in human-centric environments and interact with everyday objects, mastering dexterous manipulation is critical for real-world deployment. Traditional approaches, such as reinforcement learning and imitation learning, have made significant strides, but they often struggle due to the unique challenges of real-world dexterous manipulation, including high-dimensional control, limited training data, and covariate shift. This survey provides a comprehensive overview of these challenges and reviews existing learning-based methods for real-world dexterous manipulation, spanning imitation learning, reinforcement learning, and hybrid approaches. A promising yet underexplored direction is interactive imitation learning, where human feedback actively refines a robots behavior during training. While interactive imitation learning has shown success in various robotic tasks, its application to dexterous manipulation remains limited. To address this gap, we examine current interactive imitation learning techniques applied to other robotic tasks and discuss how these methods can be adapted to enhance dexterous manipulation. By synthesizing state-of-the-art research, this paper highlights key challenges, identifies gaps in current methodologies, and outlines potential directions for leveraging interactive imitation learning to improve dexterous robotic skills.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
