从多任务离线数据中自动发现可解释的机器人技能，提升复杂行为学习效率。
Offline Discovery of Interpretable Skills from Multi-Task Trajectories
- 分三阶段：先粗分割再微调，用弱监督和自监督结合发现技能边界
- 在D4RL Kitchen上成功率超基线，新任务组合也能成功复用技能
- 发现的技能语义清晰、可组合，适合需要可解释性的机器人系统
层次化模仿学习是通过示范获取复杂机器人行为的强大范式。核心挑战在于从长时序、多任务的离线数据中发现可复用的技能，尤其当数据缺乏显式奖励或子任务标注时。本文提出LOKI，一个三阶段端到端学习框架，用于离线技能发现与层次化模仿。第一阶段利用弱任务标签，通过强化对齐的向量量化变分自编码器进行粗粒度、任务感知的宏观分段；第二阶段采用自监督序列模型对分段进行微观细化，并通过迭代聚类整合技能边界；第三阶段基于精确边界构建选项式层次策略，包含学习到的终止条件β以实现显式技能切换。LOKI在具有挑战性的D4RL Kitchen基准上取得高成功率，优于标准HIL基线。进一步证明，所发现技能语义合理，符合人类直觉，且具备组合性，能有效编排解决未见新任务。
原文摘要 · Abstract (English)
Hierarchical Imitation Learning is a powerful paradigm for acquiring complex robot behaviors from demonstrations. A central challenge, however, lies in discovering reusable skills from long-horizon, multi-task offline data, especially when the data lacks explicit rewards or subtask annotations. In this work, we introduce LOKI, a three-stage end-to-end learning framework designed for offline skill discovery and hierarchical imitation. The framework commences with a two-stage, weakly supervised skill discovery process: Stage one performs coarse, task-aware macro-segmentation by employing an alignment-enforced Vector Quantized VAE guided by weak task labels. Stage two then refines these segments at a micro-level using a self-supervised sequential model, followed by an iterative clustering process to consolidate skill boundaries. The third stage then leverages these precise boundaries to construct a hierarchical policy within an option-based framework-complete with a learned termination condition beta for explicit skill switching. LOKI achieves high success rates on the challenging D4RL Kitchen benchmark and outperforms standard HIL baselines. Furthermore, we demonstrate that the discovered skills are semantically meaningful, aligning with human intuition, and exhibit compositionality by successfully sequencing them to solve a novel, unseen task.
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