Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames

TL;DR

This paper introduces the Affordance Coordinate Frame (ACF), a novel representation that combines affordance and category-level pose to improve object perception and manipulation in unstructured environments.

Contribution

The paper proposes ACF, a new method that enhances perception of object affordances and manipulation poses, outperforming existing methods in detection and pose estimation.

Findings

ACF outperforms state-of-the-art object detection methods.

ACF improves category-level pose estimation accuracy.

ACF demonstrates successful application in robot manipulation tasks.

Abstract

In order to enable robust operation in unstructured environments, robots should be able to generalize manipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the task. To achieve this, we aim to provide robust and generalized perception of object affordances and their associated manipulation poses for reliable manipulation. In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF). With ACF, we represent each object class in terms of individual affordance parts and the compatibility between them, where each part is associated with a part category-level pose for robot manipulation. In our experiments, we demonstrate that ACF outperforms state-of-the-art methods for object detection, as well as category-level pose estimation for object parts. We further demonstrate the applicability of ACF to robot manipulation tasks through experiments in a simulated environment.