使用交互关键点与 Soft Actor-Critic 方法的多机器人系统示范学习框架
Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methods
AI总结：
本文提出面向多机器人系统的 LfD 框架，通过交互关键点表征视觉示范，并结合基于分类器奖励的 Soft Actor-Critic 强化学习掌握未见过的接触技能。
摘要：
示范学习（Learning from Demonstration，LfD）是一种很有前景的方法，可使多机器人系统（Multi-Robot Systems，MRS）获得复杂技能与行为。然而，MRS 中复杂的交互与协同挑战给有效的 LfD 带来了重大障碍。本文提出一种专门为 MRS 设计的新型 LfD 框架，该框架利用视觉示范来捕捉机器人—机器人以及机器人—物体之间的交互并从中学习。我们的框架引入交互关键点（Interaction Keypoints，IKs）概念，将视觉示范转换为一种表征，从而便于推断完成任务所需的各种技能。随后，机器人在需要时使用感觉运动动作和强化学习（RL）策略执行任务。我们方法的一个关键特性是能够处理示范过程中出现的、未见过的基于接触的技能。在这种情况下，系统采用 RL，并使用基于分类器的奖励函数来学习该技能，从而无需人工奖励工程，并确保对环境变化的适应性。我们在一系列移动机器人任务上评估了该框架，涵盖基于行为和基于接触的领域。结果表明，我们的方法能够有效使机器人从视觉示范中学习复杂的多机器人任务与行为。
英文摘要：
Learning from Demonstration (LfD) is a promising approach to enable Multi-Robot Systems (MRS) to acquire complex skills and behaviors. However, the intricate interactions and coordination challenges in MRS pose significant hurdles for effective LfD. In this paper, we present a novel LfD framework specifically designed for MRS, which leverages visual demonstrations to capture and learn from robot-robot and robot-object interactions. Our framework introduces the concept of Interaction Keypoints (IKs) to transform the visual demonstrations into a representation that facilitates the inference of various skills necessary for the task. The robots then execute the task using sensorimotor actions and reinforcement learning (RL) policies when required. A key feature of our approach is the ability to handle unseen contact-based skills that emerge during the demonstration. In such cases, RL is employed to learn the skill using a classifier-based reward function, eliminating the need for manual reward engineering and ensuring adaptability to environmental changes. We evaluate our framework across a range of mobile robot tasks, covering both behavior-based and contact-based domains. The results demonstrate the effectiveness of our approach in enabling robots to learn complex multi-robot tasks and behaviors from visual demonstrations.
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。
