Interactive Semantics-Enhanced Vision-Language Model-Driven Hypergraph Reasoning for Robotic Decision-Making in Proactive Human–Robot Collaboration
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it