DexArt：面向铰接物体的通用灵巧操作基准
DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects
AI总结：
DexArt是一个物理模拟器中的灵巧操作基准，通过强化学习与3D表示学习，评估多指手对未见铰接物体的泛化操作能力。
摘要：
为了实现通用型机器人，我们需要机器人像人类一样操作日常铰接物体。当前的机器人操作严重依赖平行夹爪，这限制了机器人只能操作有限种类的物体。另一方面，使用多指机器人手能够更好地逼近人类行为，并使机器人能够操作多样化的铰接物体。为此，我们提出了一个新基准，名为DexArt，它涉及在物理模拟器中对铰接物体进行灵巧操作。在我们的基准中，我们定义了多个复杂的操作任务，机器人手需要在每个任务中操作多样化的铰接物体。我们的主要焦点是评估所学策略在未见过的铰接物体上的泛化能力。考虑到手和物体都具有高自由度，这非常具有挑战性。我们使用强化学习结合3D表示学习来实现泛化。通过广泛的研究，我们提供了关于3D表示学习如何影响基于3D点云输入的强化学习决策的新见解。更多细节可在https://www.chenbao.tech/dexart/ 找到。
英文摘要：
To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects. On the other hand, operating with a multi-finger robot hand will allow better approximation to human behavior and enable the robot to operate on diverse articulated objects. To this end, we propose a new benchmark called DexArt, which involves Dexterous manipulation with Articulated objects in a physical simulator. In our benchmark, we define multiple complex manipulation tasks, and the robot hand will need to manipulate diverse articulated objects within each task. Our main focus is to evaluate the generalizability of the learned policy on unseen articulated objects. This is very challenging given the high degrees of freedom of both hands and objects. We use Reinforcement Learning with 3D representation learning to achieve generalization. Through extensive studies, we provide new insights into how 3D representation learning affects decision making in RL with 3D point cloud inputs. More details can be found at https://www.chenbao.tech/dexart/.
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。
