用对抗协作机制训练双臂机器人抓物，效果提升一倍。
Learning Dexterous Bimanual Catch Skills through Adversarial-Cooperative Heterogeneous-Agent Reinforcement Learning
- 双臂机器人通过对抗性训练，一手扔物一手接球。
- 在15种物体上抓取奖励提升约2倍。
- 适合研究多智能体强化学习与复杂操控的学者。
传统机器人抓取多为单手系统，难以处理较大或复杂的物体。相比之下，双臂抓取虽具更高灵巧性与操作潜力，但协调与控制难度显著增加。本文提出一种基于异构智能体强化学习（HARL）的新型框架，用于学习灵巧的双臂抓取技能。方法引入对抗性奖励机制：投掷智能体动态调整抛掷速度以增加难度，而抓取智能体则学习协调双手应对不断变化的挑战。我们在模拟环境中对15种不同物体进行了评估，结果表明该方法在多样物体上表现出强鲁棒性与泛化能力。相比单智能体基线，本方法在15种物体上平均抓取奖励提升约2倍。
原文摘要 · Abstract (English)
Robotic catching has traditionally focused on single-handed systems, which are limited in their ability to handle larger or more complex objects. In contrast, bimanual catching offers significant potential for improved dexterity and object handling but introduces new challenges in coordination and control. In this paper, we propose a novel framework for learning dexterous bimanual catching skills using Heterogeneous-Agent Reinforcement Learning (HARL). Our approach introduces an adversarial reward scheme, where a throw agent increases the difficulty of throws-adjusting speed-while a catch agent learns to coordinate both hands to catch objects under these evolving conditions. We evaluate the framework in simulated environments using 15 different objects, demonstrating robustness and versatility in handling diverse objects. Our method achieved approximately a 2x increase in catching reward compared to single-agent baselines across 15 diverse objects.
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