面向Sim2Real铰接物体操作的部件引导3D RL
Part-Guided 3D RL for Sim2Real Articulated Object Manipulation
AI总结：
本文提出一种无需演示的部件引导3D RL框架，结合2D分割与帧一致不确定性感知采样策略学习紧凑3D表示，有效提升了铰接物体操作策略的训练效率及真实机器人部署稳定性，并在仿真和真实环境中展现出对未见实例的强泛化能力。
摘要：
通过视觉反馈操作未见的铰接物体对真实机器人而言是一项关键但具挑战性的任务。现有的基于学习的解决方案主要侧重于视觉可供性学习或其他预训练视觉模型来引导操作策略，这在真实场景的新实例中面临挑战。本文提出了一种新颖的部件引导3D RL框架，能够在无演示的情况下学习操作铰接物体。我们结合2D分割与3D RL的优势，提升RL策略训练的效率。为提高策略在真实机器人上的稳定性，我们设计了帧一致不确定性感知采样（FUS）策略，以获取紧凑且层次化的3D表示。此外，单一通用RL策略可在仿真中同时针对多种铰接物体操作任务进行训练，并对新类别和实例展现出出色的泛化能力。实验结果证明了该框架在仿真和真实环境中的有效性。代码发布于https://github.com/THU-VCLab/Part-Guided-3D-RL-for-Sim2Real-Articulated-Object-Manipulation。
英文摘要：
Manipulating unseen articulated objects through visual feedback is a critical but challenging task for real robots. Existing learning-based solutions mainly focus on visual affordance learning or other pre-trained visual models to guide manipulation policies, which face challenges for novel instances in real-world scenarios. In this paper, we propose a novel part-guided 3D RL framework, which can learn to manipulate articulated objects without demonstrations. We combine the strengths of 2D segmentation and 3D RL to improve the efficiency of RL policy training. To improve the stability of the policy on real robots, we design a Frame-consistent Uncertainty-aware Sampling (FUS) strategy to get a condensed and hierarchical 3D representation. In addition, a single versatile RL policy can be trained on multiple articulated object manipulation tasks simultaneously in simulation and shows great generalizability to novel categories and instances. Experimental results demonstrate the effectiveness of our framework in both simulation and real-world settings. Our code is available at https://github.com/THU-VCLab/Part-Guided-3D-RL-for-Sim2Real-Articulated-Object-Manipulation.
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。
