Bibliographic record
Abstract
The capability to perform dexterous operations in an autonomous manner would greatly enhance the productivity of space robotic operations. In the present thesis, we address this problem and propose a new methodology for vision-guided automatic grasping using SARAH, a three-fingered robotic hand. The Canadian Space Agency is planning to install SARAH on the International Space Station and we developed a methodology allowing this robotic hand to grasp objects autonomously by using a vision system in combination with an off-line grasp planner. More specifically, a new algorithm for synthesizing force-closure grasps for planar and revolute objects with SARAH is formulated. The grasp synthesis explicitly takes into account the hand geometry constraints and optimizes several grasp quality criteria simultaneously. Subsequently, a vision-based approach for object pose identification is presented. A vision system incorporating a monocular camera mounted in the center of SARAH's palm was designed. Color information taken from a region of interest in the image and color indexing are used to locate the object in the scene. The virtual images predicting the appearance of the object are utilized for pose identification. The complete methodology is practical, ensures high quality grasps and provides an intuitive tool for automatic grasping. Numerical results are presented for grasp synthesis of several objects with SARAH. These demonstrate the feasibility and optimality of the synthesized grasps, as well as the completeness and correctness of the methodology. Experiments were also conducted with SARAH as the end-effector of a 7-Degree-of-Freedom robot and vision-guided automatic grasping was achieved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".