Effective robotic systems for long-horizon human-robot collaboration must adapt to a wide range of human partners, whose physical behavior, willingness to assist, and understanding of the robot's capabilities may change over time. This demands a tightly coupled communication loop that grants both agents the flexibility to propose, accept, or decline requests as they coordinate toward completing the task effectively. We apply a Mixed-Initiative dialog paradigm to Collaborative human-roBot teaming and propose MICoBot, a system that handles the common scenario where both agents, using natural language, take initiative in formulating, accepting, or rejecting proposals on who can best complete different steps of a task. To handle diverse, task-directed dialog, and find successful collaborative strategies that minimize human effort, MICoBot makes decisions at three levels: (1) a meta-planner considers human dialog to formulate and code a high-level collaboration strategy, (2) a planner optimally allocates the remaining steps to either agent based on the robot's capabilities (measured by a simulation-pretrained affordance model) and the human's estimated availability to help, and (3) an action executor decides the low-level actions to perform or words to say to the human. In physical robot trials with 18 unique human participants, MICoBot significantly improves task success and user experience over a pure LLM baseline and standard agent allocation models. See additional videos and materials at https://robin-lab.cs.utexas.edu/MicoBot/.

翻译：面向长时程人机协作的有效机器人系统必须适应广泛的人类合作伙伴，因为其物理行为、协助意愿及对机器人能力的理解可能随时间变化。这需要一个紧密耦合的通信循环，使双方在执行任务过程中能灵活地提出、接受或拒绝请求。我们将混合主动对话范式应用于协作式人机团队，提出MICoBot系统。该系统处理以下常见场景：双方通过自然语言主动发起、接受或拒绝关于谁能最优执行任务不同步骤的提议。为处理多样化的任务导向对话，并找到能最小化人类工作量的成功协作策略，MICoBot在三个层面进行决策：(1) 元规划器通过分析人类对话来制定并编码高层协作策略；(2) 规划器基于机器人能力（通过仿真预训练的可供性模型衡量）和人类预估协助可用性，将剩余步骤最优分配给任一主体；(3) 动作执行器决定执行的具体底层动作或向人类表达的语句。在18位不同人类参与者参与的实体机器人实验中，相较于纯LLM基线和标准智能体分配模型，MICoBot显著提升了任务成功率和用户体验。更多视频资料请访问：https://robin-lab.cs.utexas.edu/MicoBot/。