Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
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 r...
| Authors | Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 May 2026 |
| Last updated at source | 18 May 2026 |
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