Robot Navigation with Interaction-based Deep Reinforcement Learning
Bibliographic record
Abstract
For the scene of dense crowd flow in limited space, it is very important and challenging for the robot to walk through the dense crowd without collision and move to the destination efficiently. As deep reinforcement learning has achieved certain results in human-aware navigation policies, it provides a feasible solution for the robot navigation in dense crowd. But current environment representation method is difficult to represent the intention of human movement, which causes that the policy network cannot make forward-looking decisions. And the previous learning model could not effectively represent any number of pedestrians and maintain stable navigation capability in unfamiliar environment. In this study, we propose a novel model of robot navigation, that is called robot human interaction reinforcement learning (RHIRL). A new environment representation method is proposed which implicitly includes the potential interaction and effectively improves the navigation ability in unfamiliar and dynamic interactive environment. The experiment results show that the proposed model has obvious advantages and excellent navigation performance in dynamic and unfamiliar environment.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".