We introduce Constrained Human-AI Cooperation (CHAIC), an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents. In CHAIC, the goal is for an embodied agent equipped with egocentric observations to assist a human who may be operating under physical constraints -- e.g., unable to reach high places or confined to a wheelchair -- in performing common household or outdoor tasks as efficiently as possible. To achieve this, a successful helper must: (1) infer the human's intents and constraints by following the human and observing their behaviors (social perception), and (2) make a cooperative plan tailored to the human partner to solve the task as quickly as possible, working together as a team (cooperative planning). To benchmark this challenge, we create four new agents with real physical constraints and eight long-horizon tasks featuring both indoor and outdoor scenes with various constraints, emergency events, and potential risks. We benchmark planning- and learning-based baselines on the challenge and introduce a new method that leverages large language models and behavior modeling. Empirical evaluations demonstrate the effectiveness of our benchmark in enabling systematic assessment of key aspects of machine social intelligence. Our benchmark and code are publicly available at https://github.com/UMass-Foundation-Model/CHAIC.

翻译：我们提出了受限人机协作（CHAIC），这是一项旨在测试具身智能体社会感知与协作能力的包容性具身社会智能挑战。在CHAIC中，目标是为配备以自我为中心观测能力的具身智能体，协助可能受限于物理条件——例如无法触及高处或受限于轮椅——的人类，以尽可能高效的方式完成常见家庭或户外任务。为实现这一目标，成功的协助者必须：（1）通过跟随人类并观察其行为来推断其意图与限制（社会感知），以及（2）制定适应人类伙伴的协作计划，以团队合作形式尽快完成任务（协作规划）。为建立该挑战的基准，我们创建了四种具有真实物理限制的智能体，以及八个包含室内外场景、多种限制条件、紧急事件与潜在风险的长期任务。我们在该挑战上评估了基于规划与学习的基线方法，并提出了一种利用大语言模型与行为建模的新方法。实证评估表明，我们的基准能有效支持对机器社会智能关键方面的系统性评估。我们的基准与代码已在 https://github.com/UMass-Foundation-Model/CHAIC 公开。