Adapting to Frequent Human Direction Changes in Autonomous Frontal Following Robots
Bibliographic record
Abstract
This letter addresses the challenge of robot follow ahead applications where the human behavior is highly variable. We propose a novel approach that does not rely on single human trajectory prediction but instead considers multiple potential future positions of the human, along with their associated probabilities, in the robot's decision-making process. We trained an LSTM-based model to generate a probability distribution over the human's future actions. These probabilities, along with different potential actions and future positions, are integrated into the tree expansion of Monte Carlo Tree Search (MCTS). Additionally, a trained Reinforcement Learning (RL) model is used to evaluate the nodes within the tree. By incorporating the likelihood of each possible human action and using the RL model to assess the value of the different trajectories, our approach enables the robot to effectively balance between focusing on the most probable future trajectory and considering all potential trajectories. This methodology enhances the robot's ability to adapt to frequent and unpredictable changes in human direction, improving its navigation and ability to navigate in front of the person.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".