■机器人装配任务需要精确的操纵和协调,通常需要先进的学习技术来实现高效和有效的表现。虽然使用基本策略的剩余强化学习在这个领域已经显示出了希望,现有的基本政策方法通常依赖于手工设计的全州功能和政策或广泛的演示,限制了它们在半结构化环境中的适用性。
■在这项研究中,我们提出了一种创新的以对象为中心的模仿和残差强化学习(OEC-IRRL)方法,该方法利用以对象为中心的(OEC)任务表示将视觉模型与模仿和残差学习集成在一起。通过使用单个演示并最大限度地减少与环境的交互,我们的方法旨在提高学习效率和效果。所提出的方法包括三个关键步骤:创建以对象为中心的任务表示,使用过点移动原语对基本策略进行模仿学习,以推广到不同的设置,并在组装阶段利用剩余RL进行不确定性感知策略改进。
■通过一系列综合实验,我们研究了OEC任务表示对基础和残差策略学习的影响,并证明了该方法在半结构化环境中的有效性。我们的结果表明,这种方法,只需要一次演示和不到1.2小时的交互,成功率提高了46%,装配时间缩短了25%。
■这项研究为机器人装配任务提供了一个有希望的途径,提供一个可行的解决方案,而不需要专业知识或定制夹具。
UNASSIGNED: Robotic assembly tasks require precise manipulation and coordination, often necessitating advanced learning techniques to achieve efficient and effective performance. While residual reinforcement learning with a base policy has shown promise in this domain, existing base policy approaches often rely on hand-designed full-state features and policies or extensive demonstrations, limiting their applicability in semi-structured environments.
UNASSIGNED: In this study, we propose an innovative Object-Embodiment-Centric Imitation and Residual Reinforcement Learning (OEC-IRRL) approach that leverages an object-embodiment-centric (OEC) task representation to integrate vision models with imitation and residual learning. By utilizing a single demonstration and minimizing interactions with the environment, our method aims to enhance learning efficiency and effectiveness. The proposed method involves three key steps: creating an object-embodiment-centric task representation, employing imitation learning for a base policy using via-point movement primitives for generalization to different settings, and utilizing residual RL for uncertainty-aware policy refinement during the assembly phase.
UNASSIGNED: Through a series of comprehensive experiments, we investigate the impact of the OEC task representation on base and residual policy learning and demonstrate the effectiveness of the method in semi-structured environments. Our results indicate that the approach, requiring only a single demonstration and less than 1.2 h of interaction, improves success rates by 46% and reduces assembly time by 25%.
UNASSIGNED: This research presents a promising avenue for robotic assembly tasks, providing a viable solution without the need for specialized expertise or custom fixtures.