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Inspired by the brain's rapid sensory adaptation, BIDETA combines rapid support memory, support-conditioned spectral graphs, and reliability-gated recurrence to preserve pretrained representations, repair sensor-dependent feature neighborhoods, and integrate reliable cross-query evidence.
This work formalizes the reasoning-execution boundary as a typed contract and constrains language-level decisions through schema-validated tool calls defined by the Model Context Protocol, rejecting malformed commands before they reach the robot.
This work proposes a hierarchical closed-loop agentic LLM-based framework to ensure robust multi-robot manipulation and achieves superior success rates, ensures robust adaptability ranging from single to cross workspace manipulation, and offers a generalizable approach for diverse manipulation tasks.
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