Computing fault tolerant motions for a robot manipulator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We introduce a method of planning fault tolerant trajectories based on the least constraint (LC) framework. Fault tolerance is achieved in two ways: exploiting properties of LC itself, and using a performance measure which assess the fault tolerant potential of a given configuration. LC encourages designs which are based solely on salient constraints of the task, allowing the inherent redundancy of the robot to be used to maintain a safe configuration. We compute the effects of faults on the topology of the configuration space and construct optimal recovery motions for a set of faults. We describe an efficient algorithm for computing the optimal recovery motions for a large number of faults over the entire configuration space simultaneously. A performance measure, called longevity, quantifies the ability of the recovery motions to complete the task. From the performance measure fault tolerant paths are constructed. We look at the simple task of positioning the end effector of a Puma 560 at a given point in the workspace.
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 | 0.000 |
| 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) | 0.000 | 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 it