CoRE：通过单一策略学习协作角色专家实现去中心化协作操作
CoRE: Learning Collaboration-Role Experts for Decentralized Collaborative Manipulation with One Policy
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中文总结 AI 辅助
CoRE通过单一策略学习协作角色专家，利用交叉注意力专家和动作-专家对齐损失，实现去中心化多机器人协作操作，在仿真和物理实验中均表现优异。
中文摘要 AI 辅助
协作操作要求机器人在交互过程中执行互补动作。我们研究单一策略的去中心化协作：每个机器人运行相同的策略，仅基于其视觉观察和本体感觉，无需任务提示、身份标签或机器人间消息。挑战在于在共享参数内学习互补的团队行为，并从每个机器人的局部观察中选择合适的动作。我们提出CoRE，从汇集的多任务、多机器人演示中学习协作角色专家。融合的外观和几何特征提供局部交互证据。查询条件交叉注意力专家提供可适应的预测路径，本地路由器在每个动作块位置组合这些路径。训练期间，动作-专家对齐损失利用固定输入下相对于演示的相对强制路由预测误差来监督专家选择，无需角色标签。在仿真基准测试中，CoRE在评估的去中心化方法中取得了最高的平均性能。物理实验展示了在多样化操作任务中的有效协作以及对伙伴延迟和减速的鲁棒性。项目页面：此https URL。
英文摘要
Collaborative manipulation requires robots to perform complementary actions as interactions unfold. We study single-policy decentralized collaboration: every robot runs the same policy from its visual observations and proprioception, without task prompts, identity labels, or inter-robot messages. The challenge is to learn complementary team behaviors within shared parameters and select appropriate actions from each robot's local observations. We introduce CoRE, which learns Collaboration-Role Experts from pooled multi-task, multi-robot demonstrations. Fused appearance and geometry provide local interaction evidence. Query-conditioned cross-attention experts provide adaptable prediction paths, which a local router combines at each action-chunk position. During training, an action-expert alignment loss supervises expert selection using relative forced-route prediction errors against demonstrations under fixed inputs, without role labels. Across simulation benchmarks, CoRE achieves the highest average performance among evaluated decentralized methods. Physical experiments demonstrate effective collaboration across diverse manipulation tasks and robustness to partner delays and slowdowns. Project page: https://aus.bot/research/core/.
发表机构
- The University of Sydney（悉尼大学）
- Zhejiang University of Technology（浙江工业大学）
机构由 AI 辅助整理，请以论文原文为准。
