37,000 Human-Planned Robotic Grasps With Six Degrees of Freedom
Bibliographic record
Abstract
Much recent work in grasp planning has focused on data-driven approaches, using deep learning to map from images to gripper configurations. However, this approach typically fails about once per ten attempts, limiting its practicality. We sought to better understand the degree to which such failures can be attributed to hardware versus control. To this end, we developed a naturalistic grasp demonstration system in which a gripper was fitted with a handle and moved by a human operator, while its trajectory was recorded with a motion tracker. The gripper's fingers were controlled with a joystick. We recorded roughly 37 K grasp demonstrations with this system. These grasps were almost always successful. In contrast with planar grasp planners that perform only top-down grasps by design, many of the human-planned grasps used a horizontal approach rather than a top-down approach. We analysed robustness ofa subset of these human-planned grasps, and found that many were robust to gripper rotations of about π/8 radians and translations of 3 cm (depending on the object). Consistent with past work, human operators tended to align the gripper aperture with objects' principal axes. We also tested robustness of grasps in which the gripper aperture was aligned exactly with the principal axes, and found that these heuristic grasps were even more robust than human-planned grasps. This suggests that humans used a partly symbolic grasp planning strategy, with somewhat imprecise control.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".