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.

翻译：传统的机器人抓取研究主要集中于单手机器人系统，这类系统在处理较大或较复杂物体时存在能力局限。相比之下，双手抓取在提升灵巧性和物体操控能力方面具有显著潜力，但同时也带来了协调与控制方面的新挑战。本文提出一种基于异构智能体强化学习（HARL）的灵巧双手抓取技能学习新框架。该方法引入了一种对抗性奖励机制：其中投掷智能体通过调整速度来增加投掷难度，而抓取智能体则学习在动态变化的条件下协调双手完成物体抓取。我们在仿真环境中使用15种不同物体对该框架进行评估，结果表明其在处理多样化物体时具有鲁棒性和通用性。与单智能体基线方法相比，我们的方法在15种不同物体上的抓取奖励提升了约2倍。