用16自由度机械手在拥挤环境中精准分离目标物体。
Learning to Singulate Objects in Packed Environments using a Dexterous Hand
- 用位移状态表示+分阶段强化学习，实现复杂堆叠下的物体分离。
- 仿真中成功分离目标物，真实机器人实验成功率79.2%。
- 适合高精度抓取与密集场景操作任务，如仓储分拣。
机器人物体分离任务要求在杂乱环境中识别、抓取并取出目标物体，难点在于遮挡及物体间相互干扰。现有方法多依赖充足空间进行长距离推移，难以应对空间紧凑、物体紧密堆积的场景。本文提出SOPE框架，采用基于位移的状态表示和多阶段强化学习策略，结合16自由度Allegro机械手完成物体分离。在Isaac Gym仿真中验证了系统在复杂堆叠环境中的有效性，并直接将训练策略迁移到真实世界。超过250次物理实验显示，该方法成功率达79.2%，优于其他学习与非学习方法。
原文摘要 · Abstract (English)
Robotic object singulation, where a robot must isolate, grasp, and retrieve a target object in a cluttered environment, is a fundamental challenge in robotic manipulation. This task is difficult due to occlusions and how other objects act as obstacles for manipulation. A robot must also reason about the effect of object-object interactions as it tries to singulate the target. Prior work has explored object singulation in scenarios where there is enough free space to perform relatively long pushes to separate objects, in contrast to when space is tight and objects have little separation from each other. In this paper, we propose the Singulating Objects in Packed Environments (SOPE) framework. We propose a novel method that involves a displacement-based state representation and a multi-phase reinforcement learning procedure that enables singulation using the 16-DOF Allegro Hand. We demonstrate extensive experiments in Isaac Gym simulation, showing the ability of our system to singulate a target object in clutter. We directly transfer the policy trained in simulation to the real world. Over 250 physical robot manipulation trials, our method obtains success rates of 79.2%, outperforming alternative learning and non-learning methods.
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