OpenHEART: Opening Heterogeneous Articulated Objects with a Legged Manipulator

TL;DR

This paper introduces a sample-efficient framework for legged robots to open diverse articulated objects by using novel feature extraction and adaptive estimation techniques, demonstrated in simulation and real-world tests.

Contribution

It presents SAFE for compact geometric encoding and ArtIEst for adaptive motion estimation, enhancing generalization and robustness in manipulation tasks.

Findings

Effective in simulation and real-world scenarios

Improved cross-domain generalization

Sample-efficient manipulation of heterogeneous objects

Abstract

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/