VeGAS improves MLLM-based embodied agents by sampling action ensembles and using a verifier trained on LLM-synthesized failure cases, yielding up to 36% relative gains on hard multi-object long-horizon tasks in Habitat and ALFRED.
Esca: Contextualizing embodied agents via scene-graph generation.arXiv preprint arXiv:2510.15963
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
VeGAS improves MLLM-based embodied agents by sampling action ensembles and using a verifier trained on LLM-synthesized failure cases, yielding up to 36% relative gains on hard multi-object long-horizon tasks in Habitat and ALFRED.
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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.