GRASP maps natural language to bounding-box goals via VLM for neuro-symbolic planning and reports 73.3% success in 90 real-robot trials without task-specific training.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Analysis reveals Pi-GCRL degradation in contact-rich tasks due to hybrid dynamics; contact-aware and hierarchical formulations are proposed to extend it to manipulation.
citing papers explorer
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Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning
GRASP maps natural language to bounding-box goals via VLM for neuro-symbolic planning and reports 73.3% success in 90 real-robot trials without task-specific training.
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Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
Analysis reveals Pi-GCRL degradation in contact-rich tasks due to hybrid dynamics; contact-aware and hierarchical formulations are proposed to extend it to manipulation.