OpenHEART: 用腿部机械臂开启异构关节物体
OpenHEART: Opening Heterogeneous Articulated Objects with a Legged Manipulator
AI总结：
本文提出OpenHEART框架，通过SAFE和ArtIEst实现腿部机械臂高效开启异构关节物体，提升跨领域泛化与样本效率。
摘要：
腿部机械臂具有高机动性和多功能操作能力。然而，与异构关节物体（如门、抽屉和柜子）进行鲁棒交互仍然具有挑战性，因为这些物体的关节类型多样，且腿部机器人的动力学复杂。现有的基于强化学习（RL）的方法通常依赖于高维感官输入，导致样本效率低下。在本文中，我们提出了一种用于用腿部机械臂开启异构关节物体的鲁棒且样本高效的框架。具体而言，我们提出了基于采样的抽象特征提取（SAFE），将把手和面板的几何结构编码为紧凑的低维表示，从而提高跨领域泛化能力。此外，引入了关节信息估计器（ArtIEst），用于自适应地混合本体感觉与外周感觉，以估计每个物体的开启方向和运动范围。所提出的框架被部署到模拟和现实世界机器人系统中，以操控各种异构关节物体。项目网站上可找到视频：https://openheart-icra.github.io/OpenHEART/
英文摘要：
Legged manipulators offer high mobility and versatile manipulation. However, robust interaction with heterogeneous articulated objects, such as doors, drawers, and cabinets, remains challenging because of the diverse articulation types of the objects and the complex dynamics of the legged robot. Existing reinforcement learning (RL)-based approaches often rely on high-dimensional sensory inputs, leading to sample inefficiency. In this paper, we propose a robust and sample-efficient framework for opening heterogeneous articulated objects with a legged manipulator. In particular, we propose Sampling-based Abstracted Feature Extraction (SAFE), which encodes handle and panel geometry into a compact low-dimensional representation, improving cross-domain generalization. Additionally, Articulation Information Estimator (ArtIEst) is introduced to adaptively mix proprioception with exteroception to estimate opening direction and range of motion for each object. The proposed framework was deployed to manipulate various heterogeneous articulated objects in simulation and real-world robot systems. Videos can be found on the project website: https://openheart-icra.github.io/OpenHEART/
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。
