Visual Servoing of Unknown Objects for Family Service Robots
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Aiming at satisfying the increasing demand of family service robots for housework, this paper proposes a robot visual servoing scheme based on the randomized trees to complete the visual servoing task of unknown objects in natural scenes. Here, “unknown” means that there is no prior information on object models, such as template or database of the object. Firstly, an object to be manipulated is randomly selected by user prior to the visual servoing task execution. Then, the raw image information about the object can be obtained and used to train a randomized tree classifier online. Secondly, the current image features can be computed using the well-trained classifier. Finally, the visual controller can be designed according to the error of image feature, which is defined as the difference between the desired image features and current image features. Five visual positioning of unknown objects experiments, including 2D rigid object and 3D non-rigid object, are conducted on a MOTOMAN-SV3X six degree-of-freedom (DOF) manipulator robot. Experimental results show that the proposed scheme can effectively position an unknown object in complex natural scenes, such as occlusion and illumination changes. Furthermore, the developed robot visual servoing scheme has an excellent positioning accuracy within 0.05 mm positioning error.
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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) | 0.001 | 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