A Probabilistic Framework for Learning Kinematic Models of Articulated Objects

TL;DR

This paper introduces a probabilistic framework for modeling and learning the kinematic structures of articulated objects, enabling robots to better understand and manipulate everyday items in domestic environments.

Contribution

It presents a novel probabilistic approach for estimating kinematic graphs of articulated objects, including structure learning and generalization to unseen objects.

Findings

Approach is valid and accurate in real robot experiments.

Framework is efficient and applicable to various robotic tasks.

Models can generalize to new, unseen objects.

Abstract

Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.