aiqbench analysis
This paper tackles robot object search by fusing vision-language model plausibility scores with geometric occupancy evidence in a shared 3D voxel belief, updated via Bayesian log-odds filtering. The core claim is that combining semantic priors with explicit spatial belief beats either alone, and the authors support it with end-to-end tests on a bimanual mobile humanoid compared against geometry-only and LLM-planning baselines. Readers should note that the evidence comes from a single hardware platform and the comparisons are against a limited set of baselines, so generality across environments remains to be shown.
Knowing Where to Look: Interactive Object Search via Spatial-Semantic Fusion
arXiv:2611.00499v1 Announce Type: new Abstract: When searching for an object, humans naturally combine semantic intuition - reasoning about where the object is likely to be stored - with geometric awareness of what parts of the space are visible, occluded, or unexplored. Effective robot object search requires the same joint reasoning, yet existing methods rely on only one: geometric methods maintain principled spatial beliefs but lack deep semantic understanding, while language-model based methods leverage commonsense knowledge but maintain no explicit belief over space. We argue that jointly reasoning over semantic and geometric evidence in an explicit 3D belief enables human-like interactive object search that outperforms either modality alone. To test this claim, we build a system that accumulates per-object VLM plausibility scores and geometric occupancy evidence via Bayesian log-odds filtering into a unified 3D voxel belief field. High-belief regions in the combined belief field drive action selection at each step: taking a closer look from a different viewpoint, navigating toward an unexplored region of the workspace, or physically interacting with a closed cabinet. We deploy this system end-to-end on a bimanual mobile humanoid and find that it outperforms geometry-only search and LLM-based planning methods. Project website - https://skymanaditya1.github.io/objsearch-website/
Abstract:When searching for an object, humans naturally combine semantic intuition - reasoning about where the object is likely to be stored - with geometric awareness of what parts of the space are visible, occluded, or unexplored. Effective robot object search requires the same joint reasoning, yet existing methods rely on only one: geometric methods maintain principled spatial beliefs but lack deep semantic understanding, while language-model based methods leverage commonsense knowledge but maintain no explicit belief over space. We argue that jointly reasoning over semantic and geometric evidence in an explicit 3D belief enables human-like interactive object search that outperforms either modality alone. To test this claim, we build a system that accumulates per-object VLM plausibility scores and geometric occupancy evidence via Bayesian log-odds filtering into a unified 3D voxel belief field. High-belief regions in the combined belief field drive action selection at each step: taking a closer look from a different viewpoint, navigating toward an unexplored region of the workspace, or physically interacting with a closed cabinet. We deploy this system end-to-end on a bimanual mobile humanoid and find that it outperforms geometry-only search and LLM-based planning methods. Project website - this https URL
Robotics (cs.RO)
arXiv:2611.00499 [cs.RO]
(or arXiv:2611.00499v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.00499 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 287 words