论文标题
物体几何形状的有效表示，用于增强交互式抓取策略的增强学习
Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies
论文作者
论文摘要
抓住不同形状和大小的物体 - 对人类的基础，轻松的技能 - 仍然是机器人技术的挑战性任务。尽管基于模型的方法可以预测已知对象模型的稳定掌握配置，但它们很难推广到新颖的对象，并且经常以非相互作用的开环方式运行。在这项工作中，我们提出了一个强化学习框架，该框架通过连续控制拟人化的机器人手来了解各种几何不同现实世界的互动抓握。我们探讨了对象几何形状的几个明确表示，作为策略的输入。此外，我们建议通过签署的距离隐式地告知政策，并表明这自然适合通过形状的奖励组成部分指导搜索。最后，我们证明了所提出的框架即使在更具挑战性的条件下也能够学习，例如从杂乱无章的垃圾箱中抓住。在这种情况下，出现了必要的预磨牙行为，例如对象重新定位和环境限制的利用。可在https：//maltemosbach.github上获得学习互动策略的视频。 io/geometry_aware_grasping_policies。
Grasping objects of different shapes and sizes - a foundational, effortless skill for humans - remains a challenging task in robotics. Although model-based approaches can predict stable grasp configurations for known object models, they struggle to generalize to novel objects and often operate in a non-interactive open-loop manner. In this work, we present a reinforcement learning framework that learns the interactive grasping of various geometrically distinct real-world objects by continuously controlling an anthropomorphic robotic hand. We explore several explicit representations of object geometry as input to the policy. Moreover, we propose to inform the policy implicitly through signed distances and show that this is naturally suited to guide the search through a shaped reward component. Finally, we demonstrate that the proposed framework is able to learn even in more challenging conditions, such as targeted grasping from a cluttered bin. Necessary pre-grasping behaviors such as object reorientation and utilization of environmental constraints emerge in this case. Videos of learned interactive policies are available at https://maltemosbach.github. io/geometry_aware_grasping_policies.
