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Bibliographic record
Abstract
Motion planning is a fundamental problem in autonomous robotics with real-time and low-energy requirements for safe navigation through a dynamic environment. More than 90% of computation time in motion planning is spent on collision detection between the robot and the environment. Several motion planning approaches, such as deep learning-based motion planning, have shown significant improvements in motion planning quality and runtime with ample parallelism available in collision detection. However, naive parallelization of collision detection queries significantly increases computation compared to sequential execution. In this work, we investigate the sources of redundant computations in coarsegrained (inter-collision detection) and fine-grained (intracollision detection) parallelism. We find that the physical spatial locality of obstacles results in redundant computation in coarse-grained parallelism. We further show that the primary sources of redundant computation in fine-grained parallelism are easy cases where objects are far apart or significantly overlapping. Based on these insights, we propose MPAccel to improve the energy efficiency of parallelization in motion planning. MPAccel consists of SAS, a Spatially Aware Scheduler for coarse-grained parallelism, and CECDUs, Cascaded Early-exit Collision Detection Units for fine-grained parallelism. SAS results in 7× speedup using 8× parallelization with 6% increase in the computation compared to 3.7× speedup with 83% increase in computation for naive parallelization. CECDU can perform collision detection in 46 -- 154 cycles for a robot with 6 degrees of freedom. We evaluate MPAccel to execute a state-of-the-art learning-based motion planning algorithm. Our simulations suggest MPAccel can achieve real-time motion planning for a robot with 7 degrees of freedom in 0.014ms-0.49ms with an average latency of 0.099ms compared to 1.42ms on a CPU-GPU system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it