Neural Encodings for Energy-Efficient Motion Planning
Bibliographic record
Abstract
Neural motion planners can increase motion planning quality and, by reducing collision detection computations, improve runtime. However, when profiled on an accelerator-rich hardware system, neural planning contributes to more than 50% of the runtime, and 33% of the computation energy consumption, motivating the design of compute- and energy-efficient neural planners. In this work, we propose a neural planner using Binary Encoded Labels (BEL), where a set of binary classifiers are used instead of a typical regression network. Compared to conventional regression-based neural planners, the proposed BEL neural planner reduces neural planning (inference) computation and collision detection checks while maintaining equal or higher motion planning success rate across various motion planning benchmarks. This computation reduction can improve the computation energy efficiency of neural planning by$1.4 \times-21.4 \times$. Finally, we demonstrate the trade-offs between collision detection and neural planning computation to maximize energy efficiency for different hardware configurations.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 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".