学习在拥挤环境中使用灵巧手分离物体
Learning to Singulate Objects in Packed Environments using a Dexterous Hand
AI总结：
本文提出SOPE框架，通过位移状态表示和多阶段强化学习，使用16自由度Allegro Hand在拥挤环境中分离物体，并在250次真实试验中取得79.2%成功率。
摘要：
机器人物体分离，即机器人在杂乱环境中必须隔离、抓取并取回目标物体，是机器人操作中的一个基本挑战。由于遮挡以及其他物体作为操作障碍，该任务非常困难。机器人在尝试分离目标时，还必须推理物体间交互的影响。先前的研究探讨了在有足够空间进行相对较长推动以分离物体的场景下的物体分离，这与空间狭窄且物体间几乎没有间隙的情况形成对比。本文提出了拥挤环境中的物体分离（SOPE）框架。我们提出了一种新方法，涉及基于位移的状态表示和多阶段强化学习过程，使用16自由度Allegro Hand实现分离。我们在Isaac Gym仿真中进行了大量实验，展示了系统在杂乱中分离目标物体的能力。我们将仿真中训练的策略直接迁移到现实世界。在超过250次物理机器人操作试验中，我们的方法获得了79.2%的成功率，优于其他学习和非学习方法。
英文摘要：
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.
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。
