MIND: Instruction-Conditioned Incremental Grounding for Robo
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: MIND: Instruction-Conditioned Incremental Grounding for Robots. MIND is a framework that enables robots to maintain target-room decisions without re-evaluating every room after each event. It uses a language model to compile instructions into dependencies, and a controller to interse…
Generated from attributed reports · Updated 58 minutes ago
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- arXiv Robotics — research abstractsMIND: Instruction-Conditioned Incremental Grounding for Robots
MIND is a framework that enables robots to maintain target-room decisions without re-evaluating every room after each event. It uses a language model to compile instructions into dependencies, and a controller to intersect these with event footprints, refreshing judgments and updating reachability and selection. MIND reduces mean semantic evaluations from 30.6 to 11.2 in simulated environments while preserving all post-event decisions.
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