Neurosymbolic Embodied Agents: Factoring Long-Horizon Household Tasks into Visual Exploration and Constrained Symbolic Planning
Score
78提出了新的神经符号两阶段具身规划框架，明确了约束解码与搜索的互补关系，并量化了token和图像开销优势。
方法描述清晰，实验覆盖多模型规模和多环境，消融对比充分，结论有说服力。
无需专门训练即可提升小模型可执行规划能力，对低资源具身智能部署有直接借鉴价值。
针对当前大模型具身规划热点问题，提供即时可验证的可行方案，时效性高。
来自arXiv预印本，未经同行评审，但实验平台和基准均为公认标准，方法严谨。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper presents a novel and efficient neurosymbolic approach with strong experiments and clear results, balancing theoretical clarity and practical potential.
AI commentary
「What stands out is that this paper doesn't rely on bigger models; instead, it uses a neurosymbolic combination to make a 4B small model shine in embodied planning. For those working on robot decision-making or Agent deployment, this constrained decoding plus search approach may be a more efficient and controllable path than pure test-time compute.」
Quick overview
Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dynamics or act on incorrectly grounded entities. We present a neurosymbolic agent that factors long-horizon household tasks into task-directed visual exploration and constrained symbolic planning. In the first phase, a vision-language model and exploration harness acquire goal-relevant predicates and instance bindings from egocentric observations and grounded interactions, producing a symbolic initial state. In the second, a PDDL transition model restricts decoding to tokens that extend applicable actions. Monte Carlo tree search then evaluates executable continuations using a domain-independent planning heuristic. The resulting plans are executable by construction under the transition model, with transfer to the environment conditioned on correct visual grounding. On VirtualHome and ALFWorld, open 4B-27B models exceed 90% success in both environments, and our smallest agent substantially outperforms a 27B direct visual policy in each. Constraints and search prove complementary rather than interchangeable: in ALFWorld either alone solves under a third of tasks, whereas their combination solves over 95%. The method also uses several times fewer generated tokens than extended thinking and far fewer model-visible images than direct interaction, and residual failures localize to state acquisition rather than plan generation without any specialized training.
- The neurosymbolic agent factors long-horizon household tasks into visual exploration and symbolic planning, first acquiring a symbolic initial state, then using PDDL-constrained decoding and MCTS search to guarantee executability.
- Open 4B-27B models exceed 90% success on both VirtualHome and ALFWorld, with the smallest agent substantially outperforming a 27B direct visual policy.
- Constraints and search are complementary: in ALFWorld, either alone solves under a third of tasks, while their combination solves over 95%.
- The method uses several times fewer generated tokens than extended thinking and far fewer model-visible images than direct interaction, with residual failures localized to state acquisition rather than plan generation.
Full text
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