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This research introduces a neurosymbolic architecture enabling AI agents to adaptively assemble novel structures using semantic constraints derived from natural language interactions post-deployment. By combining symbolic reasoning with visual observations, the system achieves data-efficient learning in scenarios where valid component configurations were unknown during training. The findings highlight significant progress toward robust embodied intelligence capable of handling open-ended physical tasks through conversational guidance.
Learning to Assemble Novel Structures with Unfamiliar Parts under Semantic Constraints
arXiv:2608.13684v1 Announce Type: new Abstract: This paper describes a neurosymbolic architecture for learning to assemble novel structures using evidence from embodied conversations and task demonstrations. We focus on scenarios where an agent encounters, after deployment, semantic constraints on structures--in other words, constraints as to which part types and features make valid structures--that were not available during training, and where it is initially unaware of the relevant structure and component part concepts. The agent must acquire and exploit such knowledge through user interactions while attempting assembly. We study this setting in a simulated toy truck assembly domain, learning from symbolic evidence encoded in natural language and from dense visual observations. Our experiments show that communicating semantic constraints through natural language (e.g., "dump trucks have a dumper") yields more data-efficient online adaptation than relying only on task demonstrations and/or only naming the parts through natural language.
Abstract:This paper describes a neurosymbolic architecture for learning to assemble novel structures using evidence from embodied conversations and task demonstrations. We focus on scenarios where an agent encounters, after deployment, semantic constraints on structures--in other words, constraints as to which part types and features make valid structures--that were not available during training, and where it is initially unaware of the relevant structure and component part concepts. The agent must acquire and exploit such knowledge through user interactions while attempting assembly. We study this setting in a simulated toy truck assembly domain, learning from symbolic evidence encoded in natural language and from dense visual observations. Our experiments show that communicating semantic constraints through natural language (e.g., "dump trucks have a dumper") yields more data-efficient online adaptation than relying only on task demonstrations and/or only naming the parts through natural language.
Artificial Intelligence (cs.AI)
arXiv:2608.13684 [cs.AI]
(or arXiv:2608.13684v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.13684 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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