Social robot navigation: a review and benchmarking of learning-based methods
Bibliographic record
Abstract
For autonomous mobile robots to operate effectively in human environments, navigation must extend beyond obstacle avoidance to incorporate social awareness. Safe and fluid interaction in shared spaces requires the ability to interpret human motion and adapt to social norms-an area that is being reshaped by advances in learning-based methods. This review examines recent progress in learning-based social navigation methods that deal with the complexities of human-robot coexistence. We introduce a taxonomy of navigation methods and analyze core system components, including realistic training environments and objectives that promote socially compliant behavior. We conduct a comprehensive benchmark of existing frameworks in challenging crowd scenarios, showing their advantages and shortcomings, while providing critical insights into the architectural choices that impact performance. We find that many learning-based approaches outperform model-based methods in realistic coordination scenarios such as navigating doorways. A key highlight is the end-to-end models, which achieve strong performance by directly planning from raw sensor input, enabling more efficient and adaptive navigation. This review also maps current trends and outlines ongoing challenges, offering a strategic roadmap for future research. We emphasize the need for models that accurately anticipate human movement, training environments that realistically simulate crowded spaces, and evaluation methods that capture real-world complexity. Advancing these areas will help overcome current limitations and move social navigation systems closer to safe, reliable deployment in everyday environments. Additional resources are available at: https://socialnavigation.github.io.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".