灵巧功能性抓取
Dexterous Functional Grasping
AI总结：
本文提出模块化灵巧功能性抓取框架，通过物体区域匹配获取可供性，并结合少量人类数据的 eigengrasp 动作空间训练仿真 RL 策略，实现稳定真实的野外物体抓握。
摘要：
尽管灵巧操作已取得显著进展，但大多数进展仍局限于手中重定向等基准任务，这些任务在现实世界中的实用性有限。灵巧手相对于两指夹爪的主要优势在于，它们能够拾取工具和其他物体（包括薄物体），并牢固地抓握以施加力。然而，该任务既需要对功能性可供性有复杂理解，也需要精确的底层控制。以往工作从人类数据中获取可供性，但这种方法无法扩展到底层控制。同样，仿真训练无法让机器人理解现实世界的语义。本文旨在结合两者的优势，以完成对野外物体的功能性抓取。我们采用模块化方法：首先通过匹配不同物体的对应区域来获得可供性，然后运行在仿真中训练的底层策略进行抓取。我们提出 eigengrasps（本征抓取）的一种新颖应用，利用少量人类数据缩小 RL 的搜索空间，并发现这会带来更稳定、物理上更真实的运动。我们发现，eigengrasp 动作空间在仿真中优于基线方法，在现实中优于硬编码抓取，并且与训练有素的人类遥操作者相当或表现更优。结果可视化和视频见 https://dexfunc.github.io/
英文摘要：
While there have been significant strides in dexterous manipulation, most of it is limited to benchmark tasks like in-hand reorientation which are of limited utility in the real world. The main benefit of dexterous hands over two-fingered ones is their ability to pickup tools and other objects (including thin ones) and grasp them firmly to apply force. However, this task requires both a complex understanding of functional affordances as well as precise low-level control. While prior work obtains affordances from human data this approach doesn't scale to low-level control. Similarly, simulation training cannot give the robot an understanding of real-world semantics. In this paper, we aim to combine the best of both worlds to accomplish functional grasping for in-the-wild objects. We use a modular approach. First, affordances are obtained by matching corresponding regions of different objects and then a low-level policy trained in sim is run to grasp it. We propose a novel application of eigengrasps to reduce the search space of RL using a small amount of human data and find that it leads to more stable and physically realistic motion. We find that eigengrasp action space beats baselines in simulation and outperforms hardcoded grasping in real and matches or outperforms a trained human teleoperator. Results visualizations and videos at https://dexfunc.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。
