Reaching and Grasping: what we can learn from psychology and robotics
Bibliographic record
Abstract
Grasping an object is an elementary behavior that looks easy for an adult. Yet, grasping an object is still a challenging topic in robotics. Classical approaches consider a sequence of sub tasks ranging from object recognition and localization, the planning of the trajectory to reach this object with the correct orientation and finally the control of the arm movements to grasp securely the object. If this approach has proved to be efficient in simple cases such as reaching a cup on an empty table, a lot of problems remain when the object or the environment is complex. Following, a lot of works have shown the interdependence and even the overlapping between the brain structures involved for real and imagined hand movements . Moreover, some recent works show that the grasping trajectory of an object is impacted by the social environment.Grasping an object in order to give it to somebody else is performed differently than picking it up to place it somewhere else. Even if the deposit place is the same, the global trajectory and especially the preparatory movement to pick the object is different. Hence, our brain is perhaps not planning the grasping as a sequence of elementary and independent subtasks. Such studies on the precise modeling of the human arm control can be found in, but is out of the scope of the present paper.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".