通过对抗-合作异构智能体强化学习学习灵巧双手抓取技能
Learning Dexterous Bimanual Catch Skills through Adversarial-Cooperative Heterogeneous-Agent Reinforcement Learning
AI总结：
针对双手机器人抓取的协调控制难题，提出基于HARL的对抗-合作框架，让投掷智能体动态增加难度、抓取智能体学习双手协作，并在15种物体仿真中取得约2倍于单智能体基线的奖励。
摘要：
机器人抓取传统上聚焦于单手系统，这类系统在处理较大或较复杂物体方面能力有限。相比之下，双手抓取在提升灵巧性和物体处理能力方面具有显著潜力，但也在协调与控制方面带来了新的挑战。本文提出一种使用异构智能体强化学习（Heterogeneous-Agent Reinforcement Learning，HARL）学习灵巧双手抓取技能的新颖框架。我们的方法引入了一种对抗性奖励机制：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协调双手，在这些不断变化的条件下抓取物体。我们在仿真环境中使用15种不同物体评估该框架，证明了其在处理多样物体时的鲁棒性和通用性。在15种多样物体上，与单智能体基线相比，我们的方法实现了约2倍的抓取奖励提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
