A Multimodal and Hybrid Framework for Human Navigational Intent Inference
Bibliographic record
Abstract
Understanding human navigational intent is essential for robots to be able to interact with and navigate around humans safely and naturally. Current methods typically perform inference through only one mode of perception such as human motion trajectory, and a single theoretical framework such as a learning-based or classical approach. In contrast, this paper studies prediction of human navigational intent using multimodal perception within a hybrid framework. Our framework consists of two modules: a) a learning-based prediction module to predict a human’s future goal position, and b) a classical control theory-inspired reconstruction module to reconstruct a possible future trajectory or a set of possible future positions using the predicted future goal position. For the prediction module, we propose an end-to-end LSTM-CNN hybrid neural network for predicting a human’s future position in the real world, given human motion, human body pose and head orientation. This visual information from an egocentric perspective is used to make predictions of a human’s future position in world space, essential for robotic navigation algorithms and planning. In the reconstruction module, we present two control theoretic methods to reconstruct possible future trajectories of human: trajectory generation for differentially flat system and reachability analysis. We evaluate the performance of our framework on a newly collected dataset called SFU-Store-Nav. Experimental results reveal that our method outperforms various baselines especially when a relatively small amount of data is available.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".