行动之前：学习用于分散式多机器人操作的动作条件化团队响应信念
Before We Act: Learning Action-Conditioned Team-Response Beliefs for Decentralized Multi-Robot Manipulation
AI总结：
该研究提出CARE框架，用于学习多机器人操作中的动作条件化团队响应信念，通过显式响应监督提升候选排序与选择效果，在多个基准任务中实现最高平均成功率。
摘要：
在多机器人操作中，一个动作的价值取决于队友的响应方式。我们提出了CARE（Cooperative Action--Response Belief Estimation，协作动作-响应信念估计）框架，用于从局部历史中学习团队响应信念。预测性信念编码器对交互进行表示，并以名义动作为条件。匹配的反应式和回放延续将每个候选动作的直接优势与队友适应所贡献的效用分离开来。候选条件化读出器与总优势一同学习该响应分布，校准后的选择会选择替代动作或保留名义动作。我们的分析刻画了响应何时会改变首选动作，以及预测和源错误如何限制可恢复的增益。在表示、提议和数据固定的情况下，显式响应监督相较于通过分支效用学习相同信息，可改善候选排序并减少选择遗憾。该益处集中在队友适应改变首选动作的决策中。在RoboFactory、MARS Control Track和物理协作任务中，CARE结合了能力强的名义策略与选择性协调调整，在评估的策略中实现了最高的平均成功率。这些结果共同表明，CARE将队友适应的预测转化为有效的分散式动作选择。项目页面：this https URL。
英文摘要：
In multi-robot manipulation, an action's value depends on how teammates respond. We present \method{} (Cooperative Action--Response Belief Estimation), a framework for learning \emph{team-response beliefs} from local history. A predictive belief encoder represents the interaction and conditions a nominal action. Matched reactive and replay continuations separate each candidate's direct advantage from the utility contributed by teammate adaptation. A candidate-conditioned readout learns this response distribution alongside total advantage, and calibrated selection chooses an alternative or retains the nominal action. Our analysis characterizes when response changes the preferred action and how prediction and source errors limit the recoverable gain. With the representation, proposals, and data fixed, explicit response supervision improves candidate ranking and reduces selection regret relative to learning the same information through branch utilities. The benefit is concentrated in decisions where teammate adaptation changes the preferred action. Across RoboFactory, the MARS Control Track, and physical collaboration tasks, CARE achieves the highest average success among the evaluated policies, combining a capable nominal policy with selective coordination adjustments. Together, these results show that CARE turns predictions of teammate adaptation into effective decentralized action selection. Project page: https://jeong-zju.github.io/care/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
