四足机器人用单摄像头实现精准全身抓取，真实场景一次成功率89%。
QuadWBG: Generalizable Quadrupedal Whole-Body Grasping
- 用强化学习构建低层5维指令执行与高层抓取感知策略
- 真实世界一次性抓取准确率达89%，能抓透明物体等挑战目标
- 适合需要复杂环境操作的四足机器人应用
具有先进操作能力的腿式机器人有望显著提升家庭事务和城市维护效率。尽管在稳健运动和精确操作方面已取得进展，但将二者无缝整合为统一的全身控制仍具挑战。本文提出一种基于单臂安装相机的模块化框架，通过强化学习（RL）实现鲁棒的5维（5D）低层指令执行策略和由新型指标——广义定向可达图（GORM）引导的抓取感知高层策略。所提系统在真实世界中实现89%的一次性抓取准确率，包括抓取透明物体等困难任务。通过大量仿真与真实实验验证，系统可有效处理从地面到高于人体高度的大范围工作空间，并完成多样化全身协同运动与操作任务。
原文摘要 · Abstract (English)
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.
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