Unobtrusive and assistive obstacle avoidance for tele-operation of ground vehicles
Bibliographic record
Abstract
This paper presents a new obstacle avoidance method that provides unobtrusive assistance to tele-operation of unmanned ground vehicles. Different from existing obstacle avoidance methods, the present method can determine whether the driving commands from an operator are safe in the presence of obstacles and can automatically adjust unsafe commands to help the operator avoid proximate obstacles. The command adjustment is done in an unobtrusive manner and conforms to the dynamic and kinematic constraints of the vehicle in order to minimize its interference to the operator. Due to its assistive and unobtrusive nature, this method can quietly share some control authority with an operator in tele-operating a vehicle, and hence it has the potential to make tele-operation of ground vehicles in challenging environments significantly easier and safer. The effectiveness of this method is demonstrated in extensive experiments in cluttered environments using military-grade tracked robots.
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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 | 0.001 | 0.001 |
| 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 | 0.002 | 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".