用符号抽象+底层模仿学习，让机器人长程规划更高效可靠。
Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
- 分层策略：高层符号决策+底层模仿控制，结合两者优势。
- 在万物体场景下，高层策略1分钟内完成规划，远超现有方法。
- 适合需要长期推理的机器人任务，尤其关注效率与可解释性。
我们研究如何构建能可靠解决长程规划问题的具身智能体。从示范中学习的模仿学习在训练机器人完成复杂操作任务方面已证明有效，但仅靠模仿学习难以生成长程计划。相比之下，高层符号抽象能实现高效且可解释的长程规划。本文提出将底层模仿学习（用于精细操控）与高层符号抽象（用于长程规划）相结合。通过构建双层策略（π^hl, π^ll），其中π^ll为从底层示范学习的神经策略，π^hl则基于底层示范的符号化抽象并结合归纳泛化构建。我们在BISON系统中实现该思想。在扩展的MetaWorld基准测试中，BISON展现出对更长时序和更多物体的任务的泛化能力，优于VLA与端到端方法，在训练与推理中也更节省时间和内存。值得注意的是，忽略底层执行时，其高层策略可在一分钟内解决包含10,000个相关物体的高层问题。
原文摘要 · Abstract (English)
We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effective in training robots to solve a diversity of complex tasks requiring fine motor control and manipulation over low-level (LL), continuous environments. Yet, it remains a difficult endeavour to generate long-horizon plans from imitation learning alone. In contrast, high-level (HL), symbolic abstractions facilitate efficient and interpretable long-horizon planning. We propose to combine the strengths of LL imitation learning for manipulation and control, and HL symbolic abstractions for long-horizon planning. We realise this idea via \emph{bilevel policies} of the form $(π^{\mathrm{hl}}, π^{\mathrm{ll}})$, consisting of a neural policy $π^{\mathrm{ll}}$ learned from LL demonstrations, and an HL symbolic policy $π^{\mathrm{hl}}$ that is constructed from symbolic abstractions of the LL demonstrations combined with inductive generalisation. We implement these ideas in the BISON system. Experiments on extended MetaWorld benchmarks demonstrate that BISON generalises to long horizons and problems with greater numbers of objects than those solved by VLA and end-to-end methods, and is more time and memory efficient in training and inference. Notably, when ignoring LL execution, BISON's HL policies can solve HL problems with 10,000 relevant objects in under a minute. Project page: https://dillonzchen.github.io/bison
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