VP-VLA decouples high-level reasoning from low-level control in VLA models by rendering spatial anchors as visual prompts directly in the RGB observation space, outperforming end-to-end baselines.
Affordgrasp: In-context affordance reasoning for open-vocabulary task-oriented grasping in clutter.arXiv preprint arXiv:2503.00778, 2025
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
MapNav uses annotated semantic maps as memory for VLN agents, claiming SOTA results in simulation and real-world tests while promising code and data release.
SynManDex generates human-like dexterous grasps for robots from synthetic human pre-grasps via retargeting and force-closure optimization, reporting 86.4% stability, 4.67/5 human-likeness, 80.7% sim success, and 83.3% real-robot success.
GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalization from sparse data.
citing papers explorer
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VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models
VP-VLA decouples high-level reasoning from low-level control in VLA models by rendering spatial anchors as visual prompts directly in the RGB observation space, outperforming end-to-end baselines.
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MapNav: A Novel Memory Representation via Annotated Semantic Maps for Vision-and-Language Navigation
MapNav uses annotated semantic maps as memory for VLN agents, claiming SOTA results in simulation and real-world tests while promising code and data release.
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SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
SynManDex generates human-like dexterous grasps for robots from synthetic human pre-grasps via retargeting and force-closure optimization, reporting 86.4% stability, 4.67/5 human-likeness, 80.7% sim success, and 83.3% real-robot success.
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General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling
GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalization from sparse data.