Fault tolerant locomotion for walking robots
Why this work is in the frame
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Bibliographic record
Abstract
We introduce a general method of planning fault tolerant motion for a robotic task based on the least constraint (LC) framework, which uses a set of constraints on the robot's configuration over time. A performance measure called longevity is defined which, for a given configuration and type of fault, describes the potential for future progress towards the goal. This measure examines the connectivity of the configuration space given failure. An algorithm for computing longevity based on dynamic programming is described. Using the longevity computed at discrete points, a path is computed which is optimally fault tolerant. The set of paths which maximize longevity form a contingency plan for faults occurring at each point. Using LC we specify a gait for a four-legged walking robot. A prototypical step is produced using the longevity measure, which we compare it to a straight-line motion implementation. The optimal longevity paths are shown to be significantly more fault tolerant than the straight-line motion.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it