CoWAM：结合世界动作模型（WAMs）的选择性策略干预协调合约
CoWAM: Coordination Contracts for Selective Policy Intervention with WAMs
AI总结：
该研究针对双机械臂机器人策略协调问题，提出结合世界动作模型（WAMs）的CoWAM协调合约选择性干预层，经8项模拟任务验证，其协调选择与闭环成功率均显著提升且有害干预极低。
AI中文摘要：
世界动作模型（WAMs）通过动作条件化的未来预测增强机器人策略，但仅靠合理未来不足以说明需要改变双机械臂策略的执行动作。本文提出CoWAM，一种将同步、角色兼容性与碰撞收敛性表达为协调合约的选择性干预层，每个合约结合类型化可接受性检查、事件条件化验证与校准干预门。CoWAM在替代方案满足所有活跃义务且提供明确低风险改进时才保留名义动作，若名义动作不可接受则调用预定义的弃权（不执行） fallback。为区分选择器质量与提案质量，所有方法在相同候选池上运行，决策在共享 oracle 标注前提交。在8项模拟双机械臂任务中，CoWAM较仅合约变体使协调有效选择提升16.7个百分点，较最强选择性基准使闭环成功率提升9.6个百分点，同时有害干预保持在1%以下，结果表明协调合约是利用预测世界-动作证据在高协调双机械臂任务中实现保守策略干预的有效接口。
英文摘要：
World Action Models (WAMs) augment robot policies with action-conditioned predicted futures, but a plausible future alone does not justify changing the action that a bimanual policy would execute. We present CoWAM, a selective intervention layer that expresses synchronization, role compatibility, and collision convergence as coordination contracts. Each contract combines typed admissibility checks with event-conditioned verification and calibrated intervention gates. CoWAM preserves the nominal action unless an alternative satisfies every active obligation and provides a clear, low-risk improvement; when the nominal action is also inadmissible, it invokes a predefined abstention fallback. To separate selector quality from proposal quality, all methods operate on identical candidate pools and commit their decisions before shared oracle labeling. Across eight simulated bimanual tasks, CoWAM improves coordination-valid selection by 16.7 percentage points over the contract-only variant and raises closed-loop success by 9.6 percentage points over the strongest selective baseline, while keeping harmful interventions below 1%. Together, these results establish coordination contracts as an effective interface for conservative policy intervention with predicted world-action evidence across coordination-rich bimanual tasks.
