GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion

TL;DR

This paper introduces GAMMA, a mobile manipulation policy that uses online grasping pose fusion to improve observation consistency and graspability assessment, enhancing robotic grasping performance.

Contribution

The work presents a novel online grasping pose fusion framework that improves observation consistency and graspability evaluation for mobile manipulation tasks.

Findings

Enhanced grasping observation consistency

Improved graspability assessment accuracy

Effective elimination of redundant and outlier grasping poses

Abstract

Mobile manipulation constitutes a fundamental task for robotic assistants and garners significant attention within the robotics community. A critical challenge inherent in mobile manipulation is the effective observation of the target while approaching it for grasping. In this work, we propose a graspability-aware mobile manipulation approach powered by an online grasping pose fusion framework that enables a temporally consistent grasping observation. Specifically, the predicted grasping poses are online organized to eliminate the redundant, outlier grasping poses, which can be encoded as a grasping pose observation state for reinforcement learning. Moreover, on-the-fly fusing the grasping poses enables a direct assessment of graspability, encompassing both the quantity and quality of grasping poses.