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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 $(\\pi^{\\mathrm{hl}}, \\pi^{\\mathrm{ll}})$, consisting of a neural policy $\\pi^{\\mathrm{ll}}$ learned from LL demonstrations, and an HL symbolic policy $\\pi^{\\mathrm{hl}}$ that is constructed from sy","title":"Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning","url":"https://arxiv.org/abs/2605.15975","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.15975v1 Announce Type: new \nAbstract: 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. 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