ArtReg: Visuo-Tactile based Pose Tracking and Manipulation of Unseen Articulated Objects

TL;DR

ArtReg introduces a visuo-tactile pose tracking method for unseen articulated objects, enabling robots to perceive and manipulate complex objects without prior knowledge, through a novel registration and control approach evaluated in real-world experiments.

Contribution

The paper presents a novel visuo-tactile pose tracking method, ArtReg, that detects articulated joints and enables goal-driven manipulation without prior object knowledge.

Findings

ArtReg achieves high pose accuracy in real robot experiments.

The method is robust under low-light and challenging backgrounds.

It outperforms existing methods on standard datasets.

Abstract

Robots operating in real-world environments frequently encounter unknown objects with complex structures and articulated components, such as doors, drawers, cabinets, and tools. The ability to perceive, track, and manipulate these objects without prior knowledge of their geometry or kinematic properties remains a fundamental challenge in robotics. In this work, we present a novel method for visuo-tactile-based tracking of unseen objects (single, multiple, or articulated) during robotic interaction without assuming any prior knowledge regarding object shape or dynamics. Our novel pose tracking approach termed ArtReg (stands for Articulated Registration) integrates visuo-tactile point clouds in an unscented Kalman Filter formulation in the SE(3) Lie Group for point cloud registration. ArtReg is used to detect possible articulated joints in objects using purposeful manipulation maneuvers such as pushing or hold-pulling with a two-robot team. Furthermore, we leverage ArtReg to develop a closed-loop controller for goal-driven manipulation of articulated objects to move the object into the desired pose configuration. We have extensively evaluated our approach on various types of unknown objects through real robot experiments. We also demonstrate the robustness of our method by evaluating objects with varying center of mass, low-light conditions, and with challenging visual backgrounds. Furthermore, we benchmarked our approach on a standard dataset of articulated objects and demonstrated improved performance in terms of pose accuracy compared to state-of-the-art methods. Our experiments indicate that robust and accurate pose tracking leveraging visuo-tactile information enables robots to perceive and interact with unseen complex articulated objects (with revolute or prismatic joints).