Bibliographic record
Abstract
Motion planning in dynamic environments is an important task for autonomous robotics. Emerging approaches employ neural networks that can learn by observing (e.g., human) experts. Such motion planners react to the environment by continually proposing candidate paths to reach a goal. Some of these candidate paths may be unsafe-i.e., cause collisions. Hence, proposed paths must be checked for safety using collision detection. We observe that $25 \%-41 \%$ of the resulting collision detection queries can be eliminated if we can anticipate which queries will return an unsafe result. We leverage this observation to propose a mechanism, COORD, to predict whether a given robot position (pose) along a proposed path will result in a collision. By prioritizing the detailed evaluation of predicted collisions, COORD enables quickly eliminating invalid paths proposed by neural network and other sampling based motion planners. COORD does this by exploiting the physical spatial locality of different robot poses and using simple hashing and saturating counters. We demonstrate the potential of collision prediction on different computation platforms, including CPU, GPU, and ASIC. We further propose a hardware collision prediction unit (COPU), and integrate it with an existing collision detection accelerator. This results in an average $17.2 \%-32.1 \%$ decrease in number of collision detection queries across different motion planning algorithms and robots. When applied to a state-of-the-art neural motion planner [41], COORD improves performance/watt by $1.23 \times$ on average for motion planning queries of varying difficulty levels. Further, we find that the benefits of collision prediction grow as the compute complexity of motion planning queries increases and provides $1.30 \times \mathrm{im}-$ provement in performance/watt in narrow passages and cluttered environments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".