Remote microscale teleoperation through virtual reality and haptic feedback
Bibliographic record
Abstract
In this paper, we address the problem of repairing previously computed plans for searching for an object. The object is sought with a 7 degrees of freedom mobile manipulator robot with an ¿eye-in-hand¿ sensor. The sensor is limited in both range and field of view. Our method computes a set of sensing configurations, which collectively cover the environment with the 3-D visibility region of the limited sensor. An order for visiting sensing configurations, which diminishes the expected value of the time for finding the object is generated. The search plan corresponds mainly to the set of sensing configurations to be visited and the order for visiting those configurations. In this paper, we show that whenever the environment changes locally our plan can also be repaired locally. We base our approach on a 3-D convex regions decomposition dividing the environment. The plan is repaired by generating a new sub-set of sensing configurations and a new order for visiting those configurations, only considering the convex regions related to the change in the map of the 3-D environment. We have implemented all our algorithms, and we present simulation results in realistic environments.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".