DockAnywhere lifts single demonstrations to diverse docking points via structure-preserving augmentation and point-cloud spatial editing to improve viewpoint generalization in visuomotor policies for mobile manipulation.
Anywherevla: Language-conditioned exploration and mobile manipulation.arXiv preprint arXiv:2509.21006
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
A policy-agnostic metric and controllable 2D grid environments with task DAGs enable measurement of exploration and exploitation errors in language model agents from observed actions.
MPVI interleaves model-based motion planning with VLAs via VLM completion checking to achieve 113% higher task progress on BEHAVIOR-1K without extra data.
LiteVLA-H delivers 19.74 Hz action tokens and 6 Hz semantic outputs on Jetson Orin via dual-rate scheduling and mixed fine-tuning, outperforming recent VLA baselines in edge action rate while preserving descriptive competence.
citing papers explorer
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DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation
DockAnywhere lifts single demonstrations to diverse docking points via structure-preserving augmentation and point-cloud spatial editing to improve viewpoint generalization in visuomotor policies for mobile manipulation.
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Exploration and Exploitation Errors Are Measurable for Language Model Agents
A policy-agnostic metric and controllable 2D grid environments with task DAGs enable measurement of exploration and exploitation errors in language model agents from observed actions.
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Make Your VLA More Robust Without More Data By Interleaving Motion Planning
MPVI interleaves model-based motion planning with VLAs via VLM completion checking to achieve 113% higher task progress on BEHAVIOR-1K without extra data.
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LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception
LiteVLA-H delivers 19.74 Hz action tokens and 6 Hz semantic outputs on Jetson Orin via dual-rate scheduling and mixed fine-tuning, outperforming recent VLA baselines in edge action rate while preserving descriptive competence.