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.

本文介绍了一项名为 'Social Rearrangement' 的任务，其中两个机器人在模拟的多智能体环境中协调完成长期任务，并通过一种称为 'Behavior Diversity Play' 的方法实现了零样本协调，促进了多样性，并达到了比基准更高的成功率和效率。