标题： 社会具身重新排列中的自适应协调 显示英文标题

标题： Adaptive Coordination in Social Embodied Rearrangement

摘要： 我们提出了“社会重新排列”任务，在模拟的多智能体环境中，该任务包括设置餐桌、整理房屋或拆卸杂货等合作日常任务。 在社会重新排列中，两个机器人协调完成一个长期任务，使用机载感知和自我中心观察，且不依赖环境的特权信息。 我们在该任务中研究零样本协作（ZSC），其中代理与新的合作伙伴协作，模拟机器人与新的人类合作伙伴协作的场景。 先前的ZSC方法在我们复杂且视觉丰富的环境中难以泛化，并进一步分析发现，它们在训练时无法生成多样化的协作行为。 为应对这一问题，我们提出行为多样性演练（BDP），这是一种新颖的ZSC方法，通过可区分性目标鼓励多样性。 我们的结果表明， BDP学习到的自适应代理能够处理视觉协作，并在未见过的环境中对新合作伙伴进行零样本泛化，相比基线方法成功率达到35%的提升，效率提高32%。 显示英文摘要

摘要： We present the task of "Social Rearrangement", consisting of cooperative everyday tasks like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi-agent environment. In Social Rearrangement, two robots coordinate to complete a long-horizon task, using onboard sensing and egocentric observations, and no privileged information about the environment. We study zero-shot coordination (ZSC) in this task, where an agent collaborates with a new partner, emulating a scenario where a robot collaborates with a new human partner. Prior ZSC approaches struggle to generalize in our complex and visually rich setting, and on further analysis, we find that they fail to generate diverse coordination behaviors at training time. To counter this, we propose Behavior Diversity Play (BDP), a novel ZSC approach that encourages diversity through a discriminability objective. Our results demonstrate that BDP learns adaptive agents that can tackle visual coordination, and zero-shot generalize to new partners in unseen environments, achieving 35% higher success and 32% higher efficiency compared to baselines.