WCM：用于通用人机交互的世界认知模型
WCM: World-Cognition Model for Generalizable Human-Robot Interaction
中文总结 AI 辅助
针对机器人在物理任务交互困难的问题，提出基于SLAK架构和异步运行时的世界认知模型（WCM），引入人在回路教学模式，经思维链监督改进模型，在九个现实世界人机交互任务中平均成功率达73.8%。
中文摘要 AI 辅助
语言智能体如今能在软件中与用户流畅交互，但机器人在物理任务中实现类似交互仍有困难。当前机器人控制范式主要针对指令执行优化，用户难以知晓动作选择原因及交互纠正教导机制。为此提出世界认知模型（WCM），基于SLAK架构和异步运行时构建，具有人在回路教学模式。教学情节和自主任务展开经思维链监督不断改进模型。在九个现实世界人机交互任务中，WCM平均成功率达73.8%。
英文摘要
Language agents can now interact fluently with users in software, but robots still struggle to bring comparable interaction to physical tasks. Current robot-control paradigms, including vision-language-action policies and world-model-based planners, are mainly optimized for instruction execution, leaving users with little visibility into why an action is chosen and few mechanisms to redirect, correct, or teach the robot through interaction. To solve this problem, we present the World-Cognition Model (WCM), a human-centered embodied agent built on the SLAK architecture (Sensing, Logic, Action, and Knowledge) and an asynchronous runtime. SLAK separates perception, reasoning, control, and memory, while the runtime allows reasoning, dialogue, and execution to proceed concurrently. WCM further introduces a human-in-the-loop teaching mode that enables users to interactively teach the robot difficult or long-horizon tasks. Teaching episodes and autonomous task rollouts are refined into chain-of-thought supervision to continually improve the model. WCM achieves a 73.8% average success rate across nine real-world human-robot interaction tasks, including tasks held out from CoT fine-tuning and a long-horizon task learned through teaching.
