GraspAgent 1.0: Adversarial Continual Dexterous Grasp Learning
Bibliographic record
Abstract
Grasp is at the core of robotic manipulation tasks. Nonetheless, most 6-DOF methods resort to a one-time setup via intensive analytics and targeting a predetermined domain. On the other hand, learning and adapting in real environments is of great promise to robotics yet challenging. In this context, this letter presents a grasp learning agent embodied in a dual-arm robot with a gripper and suction ends. Theoretically, GraspAgent is equipped with two adversarial components with the capacity for continual improvement. The first component aims to learn the synthesis of high dexterity grasps using a novelGrasp Awareness Generative Adversarial Network. Thanks to the designed set of contrastive objectives, GA-GAN yields a high rate of feasible grasps for clutter scenes. The second component is anAdversarial Experience Replay. AER promotes certain attributes during the regular rounds of training. For instance, a quality network is forced to adapt the maturity of the sampler by highlighting regions where the sampler performs well. Further, the quality network of each arm is encouraged to learn different grasp primitives, which reflects on better learning capacity. Finally, Experiments on a Yumi robot reveal a final average success rate of 93% after exploiting 17k+ feedback data. Moreover, We show that grasp sampling with GA-GAN surpasses a set of recent baseline methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".