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ARXIV:2604.17787 · ROBOTICS · SUBMITTED 21 APR · 02:40 UTC · FRESHNESS STALE
ARXIV:2604.17787ROBOTICSSUBMITTED 21 APR · 02:40 UTCFRESHNESS STALETingzheng Jia · Kan Guo · Lanping Qian · Yongli Hu · Daxin Tian · Guixian Qu · +3 at arXiv
A hierarchical framework for vision-language-action models that separates trajectory planning from residual refinement to achieve precision in robotic manipulation.
Opportunity summary
Pain A hierarchical framework for vision-language-action models that separates trajectory planning from residual refinement to achieve precision in robotic manipulation.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A hierarchical framework for vision-language-action models that separates trajectory planning from residual refinement to achieve precision in robotic manipulation. This monolithic formulation forces macro-level transport and micro-level refinement to be optimized under the same…
Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-action (VLA) policies generate actions within a single unified space. This monolithic formulation forces macro-level transport and micro-level refinement to be…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The anchor planner predicts a coarse motion scaffold, while the refinement module corrects execution-level deviations to improve geometric and contact precision. Code availability is…
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A hierarchical framework for vision-language-action models that separates trajectory planning from residual refinement to achieve precision in robotic manipulation.
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Paper Pack
10.48550/arXiv.2604.17787A hierarchical framework for vision-language-action models that separates trajectory planning from residual refinement to achieve precision in robotic manipulation.
Abstract
Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-action (VLA) policies generate actions within a single unified space. This monolithic formulation forces macro-level transport and micro-level refinement to be optimized under the same objective, causing large motions to dominate learning while suppressing small but failure-critical corrective signals. In contrast, human manipulation is structured by global movement planning together with continuous local adjustment during execution. Motivated by this principle, we propose AnchorRefine, a hierarchical framework that factorizes VLA action modeling into trajectory anchor and residual refinement. The anchor planner predicts a coarse motion scaffold, while the refinement module corrects execution-level deviations to improve geometric and contact precision. We further introduce a decision-aware gripper refinement mechanism to better capture the discrete and boundary-sensitive nature of gripper control. Experiments on LIBERO, CALVIN, and real-robot tasks demonstrate that AnchorRefine consistently improves both regression-based and diffusion-based VLA backbones, yielding gains of up to 7.8% in simulation success rate and 18% in real-world success rate.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
A hierarchical framework for vision-language-action models that separates trajectory planning from residual refinement to achieve precision in robotic manipulation. This monolithic formulation forces macro-level transport and micro-level refinement to be optimized under the same...
METHOD
Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-action (VLA) policies generate actions within a single unified space. This monolithic formulation forces macro-level transport and micro-level re...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The anchor planner predicts a coarse motion scaffold, while the refinement module corrects execution-level deviations to improve geometric and contact precision. Code availability is flagged in the produc...
WHY NOW
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 13, "author": "Tingzheng Jia; Kan Guo; Lanping Qian; Yongli Hu; Daxin Tian; Guixian Qu; Chunmian Lin; Baocai Yin; Jiapu Wang"
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A hierarchical framework for vision-language-action models that separates trajectory planning from residual refinement to achieve precision in robotic manipulation.
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Robotics
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Commercial read
7.0/10 public viability
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reason
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proof status
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passport absent
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Technical feasibility
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