DAIR：安全有效双手操作的注意力内在正则化
DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulation
摘要：
我们解决的问题，安全地解决复杂的双手机器人操作任务与稀疏奖励。这些具有挑战性的任务可以分解为子任务，这些子任务可以由不同的机器人同时或顺序执行，以提高效率。虽然以前的强化学习方法主要集中在对子任务的组合性进行建模，但在学习两个机器人的合作策略时，两个基本问题在很大程度上被忽视：（i）支配，即，一个机器人可能试图自己解决任务而使另一个机器人空闲;（ii）冲突，即，当一个机器人同时执行不同的子任务时，可能会中断另一个机器人的工作空间，这会导致不安全的碰撞。为了解决这两个问题，我们提出了一种新的技术，称为解开注意力，它提供了一个内在的正则化两个机器人专注于单独的子任务和对象。我们评估我们的方法上五双手操作任务。实验结果表明，我们提出的内在正则化成功地避免了支配，减少了冲突的政策，这导致显着更有效和更安全的合作策略比所有的基线。我们的视频项目页面位于https://mehooz.github.io/bimanual-attention。
英文摘要：
We address the problem of safely solving complex bimanual robot manipulation tasks with sparse rewards. Such challenging tasks can be decomposed into sub-tasks that are accomplishable by different robots concurrently or sequentially for better efficiency. While previous reinforcement learning approaches primarily focus on modeling the compositionality of sub-tasks, two fundamental issues are largely ignored particularly when learning cooperative strategies for two robots: (i) domination, i.e., one robot may try to solve a task by itself and leaves the other idle; (ii) conflict, i.e., one robot can interrupt another's workspace when executing different sub-tasks simultaneously, which leads to unsafe collisions. To tackle these two issues, we propose a novel technique called disentangled attention, which provides an intrinsic regularization for two robots to focus on separate sub-tasks and objects. We evaluate our method on five bimanual manipulation tasks. Experimental results show that our proposed intrinsic regularization successfully avoids domination and reduces conflicts for the policies, which leads to significantly more efficient and safer cooperative strategies than all the baselines. Our project page with videos is at https://mehooz.github.io/bimanual-attention.
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。
