Experiments on Robotic Capture of Objects in Space
Bibliographic record
Abstract
In this paper, we describe an experimental facility for studying robotic grasping of objects in space. This problem arises in several applications, including on-orbit servicing of satellites and removal of space debris. The facility is based on a novel concept for experimental evaluation of robotic capture of freefloating objects. The central idea behind it is to use a small helium airship to emulate a free-floating object. Over the past year, a facility has been developed at McGill University to implement this concept in a laboratory setting. The main components of our facility are: a seven-degree-of-freedom robot, a spherical helium airship 5 ft in diameter, a stereo-based vision system and control hardware. One key issue that had to be addressed for the airship is how to make it balanced and neutrally buoyant. Control architecture has been developed allowing the airship to fly under computer control. The function of the robot is to intercept the airship and grab it by a grapple fixture. A ‘look then move’ visual servoing architecture has been implemented allowing the robot to follow a target. The paper describes the main components of the facility and the algorithms implemented for balloon balancing, robot control and visual servoing.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".