GenCoord：私有信息下的技能路径承诺
GenCoord: Skill-Path Commitments under Private Information
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中文总结 AI 辅助
针对具身智能体协作中私有信息导致的任务协调难题，提出GenCoord框架，通过生成可执行技能路径承诺实现高效协作，在多项实验中显著提升成功率、降低决策成本与通信开销。
中文摘要 AI 辅助
假设一个具身智能体知道必须构建什么，而其队友仅知道自身工作单元可执行的转换操作。双方的局部视图都无法确定谁应行动、应传递什么，或联合任务应如何推进。我们提出GenCoord框架，它将这类私有事实对应的任务结果转化为可执行的技能路径承诺。本地部署的Qwen3.5-0.8B模型会生成多步SELF（自身）计划与peer REQ（对等请求）；当决策所需能力属于队友本地时，受限反馈条件会触发修订。解析并校验得到的承诺，将其规范化实例化后编译为Mineflayer技能，再通过交接状态与终止状态完成验证。我们采用反事实干预实验，保持环境、调用调度和执行器不变，使请求方修订与接收方执行都能双向遵循注入的任务结果。在3个独立训练的种子上，准确的能力反馈将配对的局部信息缺口从50%填补至100%。多步承诺将分布外模板的成功率提升6.9个百分点，同时减少32%的模型决策次数。在128个分布外语义簇上达到相同闭环质量时，Short DSL（短领域特定语言）相比受控自由形式通信，将对等流量降低92.8%，中位承诺生成时间缩短68.2%。这些结果表明，可执行任务结果是连接分布式局部推理与已验证联合行动的协调单元。
英文摘要
Suppose one embodied agent knows what must be built, while its teammate alone knows which transformation its workcell can perform. Neither local view determines who should act, what should be handed off, or how the joint task should continue. We introduce GenCoord, which turns the task consequence of such private facts into an executable skill-path commitment. A local Qwen3.5-0.8B model emits a multi-step SELF plan and peer REQ; bounded feedback conditions route revision when the deciding capability is peer-local. The resolved commitment is parsed, checked, canonically materialized, compiled to Mineflayer skills, and verified by handoff and terminal state. Counterfactual interventions that hold the world, call schedule, and executor unchanged make requester revision and receiver execution follow the injected task consequence in both directions. Across three independently trained seeds, correct capability feedback closes the paired local-information gap from 50% to 100%. Multi-step commitments improve held-out-template success by 6.9 points while reducing model decisions by 32%. At matched closed-loop quality on 128 held-out semantic clusters, Short DSL reduces peer traffic by 92.8% and median time-to-commitment by 68.2% relative to controlled free-form communication. These results identify executable task consequences as the coordination unit connecting distributed local reasoning to verified joint action.
发表机构
- Tsinghua University（清华大学）
- Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）
- University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
- University of California San Diego（加利福尼亚大学圣迭戈分校）
机构由 AI 辅助整理，请以论文原文为准。
