让机器人通过联合学习动作与符号结果，实现零样本技能组合。
Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
- 将动作与符号结果联合建模，构建统一生成框架。
- 无需重新训练即可在新场景中组合已有技能。
- 适合需要灵活组合动作的机器人任务研究者。
从示范学习（LfD）使机器人能从专家示例中学习复杂行为，但现有方法在未重新训练的情况下难以泛化到已知技能的新组合。现代生成策略仅建模动作轨迹分布，无法推理出稳健组合所需的符号结果。我们提出技能应联合建模动作轨迹与所引发的符号结果。为此，引入谓词动作技能（PACTS），一种闭环视觉-运动策略，将技能建模为动作与谓词信念轨迹的联合生成过程，单一模型内生成一致的动作-结果序列。联合生成动作与谓词使PACTS学习到增强动作生成与谓词分类的内部表示。此外，我们通过规划实现零样本技能组合，利用PACTS在线预测的谓词作为符号接口，实现技能序列编排与执行监控。
原文摘要 · Abstract (English)
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/
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