RetrDex: 在杂乱场景中利用灵巧手高效检索物体
RetrDex: Efficient Object Retrieval in Cluttered Scenes with a Dexterous Hand
AI总结：
本文提出RetrDex框架，通过大规模并行强化学习和空间感知表示，使灵巧臂手系统在杂乱场景中高效检索物体，展示出在多种配置下的强性能和零样本迁移能力。
摘要：
在杂乱场景中检索被遮挡的物体既具有挑战性又耗时，因为复杂的支撑关系使操作尤其困难。现有方法要么专注于支撑关系并依赖顺序抓取移除遮挡物，要么执行如推动等预备动作以促进后续抓取。然而，这些方法往往效率低下，且将物理交互视为孤立的辅助步骤。本文提出RetrDex，一种用于灵巧臂手系统的高效框架，以学习在杂乱场景中检索物体。我们的方法利用大规模并行强化学习在多样化的杂乱场景中进行训练，并结合一种空间感知的表示，该表示编码了目标、灵巧手和周围杂乱物之间的遮挡模式和空间关系。这种表示使策略能够发展出多样化的操作技能（如推动、搅拌和戳击），以主动清除遮挡物。我们评估了RetrDex在16种家庭物品上跨多种杂乱配置的表现，并在可见和不可见目标上获得强检索性能和效率。此外，我们展示了成功零样本迁移至现实世界的灵巧多指机器人系统，验证了我们方法的实用性。视频可在我们的项目网站上找到：https://RetrDex.github.io。
英文摘要：
Retrieving objects buried beneath clutter is both challenging and time-consuming, as complex support relationships make manipulation particularly difficult. Existing methods either focus on support relations and rely on sequential grasping to remove occluding objects, or perform preparatory actions such as pushing to facilitate subsequent grasps. However, these approaches are often inefficient and treat physical interactions as isolated auxiliary steps. In this paper, we propose RetrDex, an efficient framework for dexterous arm-hand systems to learn object retrieval in cluttered scenes. Our approach leverages large-scale parallel reinforcement learning (RL) in diverse cluttered scenes and incorporates a spatially aware representation that encodes occlusion patterns and spatial relationships among the target, the dexterous hand, and surrounding clutter. This representation enables the policy to develop diverse manipulation skills (e.g., pushing, stirring, and poking) that actively clear occluders. We evaluate RetrDex on 16 household objects across varied clutter configurations, and obtain strong retrieval performance and efficiency on both seen and unseen targets. Furthermore, we demonstrate successful zero-shot transfer to a real-world dexterous multi-fingered robot system, validating the practical applicability of our method. Videos can be found on our project website: https://RetrDex.github.io.
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。
