联合学习谓词和动作使零样本技能组合成为可能
Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
AI总结：
本文提出了一种联合学习谓词和动作的技能方法，通过闭合回路的视觉-运动策略，使机器人能够在不重新训练的情况下实现零样本技能组合。
摘要：
学习示范（LfD）使机器人能够从专家示例中学习复杂行为，但现有方法往往无法在不重新训练的情况下泛化到新组合的已知技能。现代生成性策略仅建模动作轨迹分布，因此无法推断出所需的符号结果。我们提出技能应联合建模动作轨迹和它们诱导的符号结果。为解决这一差距，我们引入了谓词动作技能（PACTS），一种闭合回路的视觉-运动策略，将技能建模为动作和谓词信念轨迹的联合生成过程，在单一模型中产生连贯的动作-结果滚动。联合生成动作和谓词使PACTS能够学习改进动作生成和谓词分类的内部表示。此外，我们通过利用PACTS的在线谓词预测作为符号接口来序列化和监控执行，展示了学习技能的零样本组合。项目网站：https://planpacts.github.io/
英文摘要：
Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skills without retraining. Modern generative policies model distributions over action trajectories alone, thus are unable to reason about the symbolic outcomes required for robust composition. We propose that skills should jointly model action trajectories and the symbolic outcomes they induce. To address this gap, we introduce Predicate Action Skills (PACTS), a class of closed-loop visuomotor policies that model skills as a joint generative process over action and predicate belief trajectories, producing coherent action-outcome rollouts within a single model. Jointly generating actions and predicates enables PACTS to learn internal representations that improve both action generation and predicate classification. Furthermore, we demonstrate zero-shot composition of learned skills via planning by leveraging online predicate predictions from PACTS as a symbolic interface for sequencing and monitoring execution. Project website: https://planpacts.github.io/
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。
