Adaptive Trajectory Learning With Obstacle Awareness for Motion Planning
Bibliographic record
Abstract
In motion planning, efficiently navigating from a start state to a goal state in spaces with narrow passages remains a significant challenge. Recently, learning-based methods have attracted considerable attention owing to their higher inference speeds compared to traditional approaches. However, the variability in state distribution on the expert path hinders the training of neural networks, while the overly dense states may lead to redundant decision iterations and unsatisfactory planning efficiency. In this letter, we present a novel deep learning framework for motion planning, termed Adaptive Trajectory Learning with Obstacle Awareness (ATOA). Instead of performing the conventional state-wise supervision that approaches the next state, we propose to learn the trajectory along the expert path. This mechanism not only mitigates the model's dependence on the expert paths but also has the potential to yield more effective planning solutions. Additionally, obstacle information is explicitly integrated by penalizing predictions with obstacle collisions. To further enhance the planning success rate, we introduce a confidence-driven path correction (CDPC) module to adjust the infeasible local paths. Extensive experiments demonstrate the effectiveness and superiority of ATOA compared to prior approaches in handling complex scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".