EAGOR reformulates embodied 360-degree directional reasoning as recursive Bayesian estimation on a spherical manifold using spherical harmonics, achieving training-free, rotation-equivariant target tracking.
More than a point: Capturing uncertainty with adaptive affordance heatmaps for spatial grounding in robotic tasks,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 3years
2026 3representative citing papers
SVP-IL decouples semantic reasoning from geometric grounding in vision-language-action models by injecting zero-shot spatial masks as explicit prompts into a continuous action generator, yielding higher success on ambiguous manipulation tasks with limited demonstrations.
A vision-language model outputs dual heatmaps for navigation affordance and facing to ground semantic instructions into executable free space, achieving higher affordance rates than waypoint regression across simulated robot embodiments.
citing papers explorer
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EAGOR: Embodied Reasoning in Omni-direction
EAGOR reformulates embodied 360-degree directional reasoning as recursive Bayesian estimation on a spherical manifold using spherical harmonics, achieving training-free, rotation-equivariant target tracking.
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Decoupling Semantics and Geometric Grounding: Spatial Visual Prompts for Language-Conditioned Imitation Learning
SVP-IL decouples semantic reasoning from geometric grounding in vision-language-action models by injecting zero-shot spatial masks as explicit prompts into a continuous action generator, yielding higher success on ambiguous manipulation tasks with limited demonstrations.
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Beyond Waypoints: Dual-Heatmap Grounding for Cross-Embodiment Semantic Navigation
A vision-language model outputs dual heatmaps for navigation affordance and facing to ground semantic instructions into executable free space, achieving higher affordance rates than waypoint regression across simulated robot embodiments.