Towards real-time robot simulation on uneven terrain using neural networks
Bibliographic record
Abstract
Simulation is a valuable tool for robotics research and development, and various simulation packages have been proposed. However, we are aware of no freely-available packages which implement the required fidelity to accurately model earth-moving robots that manipulate the terrain itself. The software which does exist for this is difficult if not impossible to run in real-time while achieving the desired accuracy. This paper proposes a simulation system in which a neural network is trained using data generated in a 3D high-fidelity, non-real-time simulator. The resulting neural network is used to accurately predict the motion of a robot in a 2D simulator, while also taking into consideration a height-field representing a 3D terrain. Using a trained neural network to drive the new simulation provides considerable speedup over the high-fidelity 3D simulation, allowing behaviour to be simulated in real-time while still capturing the physics of the agents and the environment.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".