Interactive Semantics-Enhanced Vision-Language Model-Driven Hypergraph Reasoning for Robotic Decision-Making in Proactive Human–Robot Collaboration
Bibliographic record
Abstract
Proactive human-robot collaboration (HRC), as a cognition-centric approach, aims to reason the dynamic process of tasks for proactive robotic decision-making, which can be represented through the spatiotemporal evolution of non-paired relationships. Most existing works rely solely on vision-driven knowledge graph methods to reason the spatiotemporal evolution. However, the spatiotemporal evolution of non-paired relationships involves the interaction of multimodal information, and understanding such interactions requires robust analytical capabilities, which poses challenges for proactive robotic decision-making. This paper proposes an interactive semantics-enhanced vision-language model-driven spatiotemporal hypergraph reasoning method (VLSHR) to reveal the spatiotemporal evolution of non-paired relationships. First, to understand vision-language semantics, we fine-tuned a vision-language large language model (LLM) with interactive semantics. Furthermore, vision-language semantics need to be transformed into a hypergraph structure that can represent non-paired relationships. To reason the spatiotemporal evolution of non-pairwise relationships in HRC, we define temporal hyperedges, spatial hyperedges, and task hyperedges, coupling the affiliations of nodes with different types of hyperedges to construct a spatiotemporal hypergraph for HRC tasks. Then, a spatiotemporal hypergraph neural network is developed to reason the spatiotemporal evolution of non-pairwise relationships for proactive robotic decision-making. Finally, a case study on HRC assembly tasks demonstrates the effectiveness of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".