Mastering Robot Control through Point-based Reinforcement Learning with Pre-training
Bibliographic record
Abstract
Visual-based Reinforcement Learning (RL) has gained prominence in robotics decision-making due to its significant potential. However, the prevalent utilization of images in visual-based RL lacks explicit descriptions of object structures and spatial configurations in scenes, thereby limiting the overall efficiency and robustness of RL in robot control. Additionally, training an RL policy solely using visual observations from scratch is typically sample-inefficient, rendering it impractical for real-world application. To address these challenges, this paper proposes a novel method, called Pre-training on Point-based RL (P2RL), which takes the point cloud representations of scenes as states and preserves the intricate spatial details between objects. To further enhance efficiency, we leverage the pre-training method to bolster the perception ability of the network. Key factors in the pre-training process are systematically examined to optimize downstream RL training. Experimental results demonstrate the superior robustness and efficiency of P2RL compared to the state-of-the-art image-based RL method, especially in evaluations involving untrained scenes.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".