QuadWBG: Generalizable Quadrupedal Whole-Body Grasping

TL;DR

This paper introduces QuadWBG, a modular RL-based framework for quadrupedal robots that achieves high-precision, generalizable whole-body grasping and manipulation in real-world scenarios, including challenging transparent objects.

Contribution

The paper presents a novel modular control system with a grasp-aware high-level policy guided by GORM, enabling robust, generalizable whole-body manipulation for quadrupedal robots.

Findings

Achieves 89% grasping accuracy in real-world tests.

Effective management of large workspace from floor to above body height.

Demonstrates success in diverse and challenging manipulation tasks.

Abstract

Legged robots with advanced manipulation capabilities have the potential to significantly improve household duties and urban maintenance. Despite considerable progress in developing robust locomotion and precise manipulation methods, seamlessly integrating these into cohesive whole-body control for real-world applications remains challenging. In this paper, we present a modular framework for robust and generalizable whole-body loco-manipulation controller based on a single arm-mounted camera. By using reinforcement learning (RL), we enable a robust low-level policy for command execution over 5 dimensions (5D) and a grasp-aware high-level policy guided by a novel metric, Generalized Oriented Reachability Map (GORM). The proposed system achieves state-of-the-art one-time grasping accuracy of 89% in the real world, including challenging tasks such as grasping transparent objects. Through extensive simulations and real-world experiments, we demonstrate that our system can effectively manage a large workspace, from floor level to above body height, and perform diverse whole-body loco-manipulation tasks.