通过时间表征对齐，让机器人学会组合基础任务完成复杂指令。
Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following
- 用当前与未来状态的表征对齐来学习任务表示
- 在无显式规划下，组合泛化性能显著提升
- 适合需要组合推理的机器人指令跟随场景
有效的任务表征应支持组合性，即在学习多种基础任务后，智能体可通过组合各步骤表征来完成多步复合任务。尽管这一思路简洁且理想，但如何自动学习具备此类组合能力的表征仍不明确。本文表明，通过引入时间对齐损失，学习当前与未来状态的表征关联，可有效提升组合泛化能力，即使在无显式子任务规划或强化学习的情况下也成立。我们在多样化的机器人操作任务及仿真环境中进行了评估，结果显示，在语言或目标图像指令下，任务表现均有显著提升。
原文摘要 · Abstract (English)
Effective task representations should facilitate compositionality, such that after learning a variety of basic tasks, an agent can perform compound tasks consisting of multiple steps simply by composing the representations of the constituent steps together. While this is conceptually simple and appealing, it is not clear how to automatically learn representations that enable this sort of compositionality. We show that learning to associate the representations of current and future states with a temporal alignment loss can improve compositional generalization, even in the absence of any explicit subtask planning or reinforcement learning. We evaluate our approach across diverse robotic manipulation tasks as well as in simulation, showing substantial improvements for tasks specified with either language or goal images.
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