Explicit Kinematic Guidance from Analytic Concepts for Vision-Language-Action Models
This paper proposes a Concept Expert module for Vision-Language-Action (VLA) models that builds executable Analytic Concepts from 3D information, providing explicit kinematic guidance and dense rewards, significantly improving success rates and learning efficiency in complex manipulation tasks.
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[Submitted on 29 Jul 2026]
Title:Explicit Kinematic Guidance from Analytic Concepts for Vision-Language-Action Models
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Abstract:Current Vision-Language-Action (VLA) models rely mainly on 2D inputs, neglecting the rich object structural information and commonsense knowledge inherent in the 3D physical world. This deficiency restricts their spatial awareness and adaptability for complex, high-precision manipulation. To bridge this crucial gap, we construct a Concept Expert module for VLA to build executable Analytic Concepts that represent objects as explicit, programmatic blueprints. Our mechanism operates in two synergistic phases: First, prior to VLA inference, the Concept Expert leverages 3D information from Vision Foundation Models (VFMs) to estimate the initial kinematic and structural parameters. Second, throughout the manipulation process, the VLA model utilizes its inherent capability to dynamically track the dynamic concept parameters, continuously aligning them with observational changes to ensure persistent accuracy. Once established, the Analytic Concepts provide explicit, high-quality guidance for VLA fine-tuning through (1) dense, programmatic manipulation rewards and (2) precise spatial guidance.
This formulation allows VLA models to learn physically grounded interaction behaviors while maintaining end-to-end learning flexibility.
Our experimental results show consistent improvements in success rate and learning efficiency across supervised and reinforcement learning settings, demonstrating the effectiveness of structured, concept-based guidance for VLA post-training.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.26513 [cs.RO]
(or arXiv:2607.26513v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.26513
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mingyang Sun [view email] [v1] Wed, 29 Jul 2026 06:24:25 UTC (1,646 KB)
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