Deep Reinforcement Learning for Robotic Grasping: Insights into Learning from Raw Visual Data
Bibliographic record
Abstract
This paper addresses the challenges of applying deep reinforcement learning (RL) to robotic grasping tasks, focusing on bridging the sim-to-real gap to ensure seamless transfer from simulated to real-world environments. We propose a two-phase modeling approach: (1) an image-free model designed to validate core functionalities and (2) an image-based model that incorporates visual data to enhance grasping precision. Using QT-Opt as a foundation, the models are trained to learn optimal grasping actions based on state-action pairs in both simulation and real settings.To stabilize training and improve learning efficiency, we employ multiple neural networks enhanced with Polyak averaging and clipped double Q-learning to address gradient instability. For action optimization in continuous control tasks, the Cross Entropy Method (CEM) is integrated to ensure robust policy learning. Additionally, we leverage the Jump-Start Reinforcement Learning (JSRL) method to improve convergence, providing initial guidance from a non-image-based agent to enhance exploration efficiency. Experimental results show the effectiveness of different reward functions and the impact of camera positioning on model performance. We also explore the use of generative frameworks to mitigate visual discrepancies between simulated and real images, improving robustness in real-world grasping tasks. We conducted comprehensive experiments to evaluate the effects of various reward functions and the influence of camera positioning on model performance.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".