Neurosymbolic Embodied Agents
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 r
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- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
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- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
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- FuzzySimilar title/name (fuzzy) · 59%datawhalechina/hello-agents →
“Fuzzy title match (0.73): “Neurosymbolic Embodied Agents” ≈ “datawhalechina/hello-agents””
- FuzzySimilar title/name (fuzzy) · 59%TauricResearch/TradingAgents →
“Fuzzy title match (0.73): “Neurosymbolic Embodied Agents” ≈ “TauricResearch/TradingAgents””
- FuzzySimilar title/name (fuzzy) · 59%Eigenwise/atomic-agents →
“Fuzzy title match (0.73): “Neurosymbolic Embodied Agents” ≈ “Eigenwise/atomic-agents””
- LinkedLinked via arxiv author · 85%Mohammad Albinhassan →
“Neurosymbolic Embodied Agents”