Socially Aware Robot Navigation Using Deep Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we propose a socially aware navigation framework for mobile service robots in dynamic human environments using a deep reinforcement learning algorithm. The primary idea of the proposed algorithm is to incorporate obstacles information (position and motion), human states (human position, human motion), social interactions (human group, human-object interaction), and social rules, e.g, minimum distances from the robot to regular obstacles, individuals, and human groups into the deep reinforcement learning model of a mobile robot. We then distribute the mobile robot into a dynamic social environment and let the mobile robot automatically learn to adapt to an embedded environment by its experiences gained through trial-and-error social interactions with the surrounding humans and objects. When the learning phase is completed, the mobile robot is able to navigate autonomously in the social environment while guaranteeing human safety and comfort with its socially acceptable behaviours.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it