用腿部机械臂高效打开各类铰链物体，突破复杂动态环境下的操作难题。
OpenHEART: Opening Heterogeneous Articulated Objects with a Legged Manipulator
- 通过抽象特征提取技术，将物体几何信息压缩为低维表示，提升泛化能力。
- 自适应融合本体与外部感知数据，准确估计每类物体的开启方向和运动范围。
- 在仿真与真实机器人上验证，可稳定操控门、抽屉等多种异构铰接物。
腿部机械臂具备高机动性和多任务操作能力，但面对门、抽屉、柜子等异构铰接物体时，由于物体结构多样且机器人动力学复杂，仍难以实现鲁棒交互。现有基于强化学习的方法通常依赖高维传感输入，导致样本效率低下。本文提出一种鲁棒且样本高效的框架，用于控制腿部机械臂打开异构铰接物体。核心方法包括：1）采样抽象特征提取（SAFE），将把手与面板几何信息编码为紧凑的低维表征，增强跨域泛化能力；2）关节信息估计器（ArtIEst），自适应融合本体感知与外部感知，以估计每个物体的开启方向与运动范围。该框架已在仿真及真实机器人系统中成功部署，可稳定操作多种异构铰接物体。相关视频见项目主页：https://openheart-icra.github.io/OpenHEART/
原文摘要 · Abstract (English)
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/
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