LASER: Learning a Latent Action Space for Efficient Reinforcement\n Learning
Bibliographic record
Abstract
The process of learning a manipulation task depends strongly on the action\nspace used for exploration: posed in the incorrect action space, solving a task\nwith reinforcement learning can be drastically inefficient. Additionally,\nsimilar tasks or instances of the same task family impose latent manifold\nconstraints on the most effective action space: the task family can be best\nsolved with actions in a manifold of the entire action space of the robot.\nCombining these insights we present LASER, a method to learn latent action\nspaces for efficient reinforcement learning. LASER factorizes the learning\nproblem into two sub-problems, namely action space learning and policy learning\nin the new action space. It leverages data from similar manipulation task\ninstances, either from an offline expert or online during policy learning, and\nlearns from these trajectories a mapping from the original to a latent action\nspace. LASER is trained as a variational encoder-decoder model to map raw\nactions into a disentangled latent action space while maintaining action\nreconstruction and latent space dynamic consistency. We evaluate LASER on two\ncontact-rich robotic tasks in simulation, and analyze the benefit of policy\nlearning in the generated latent action space. We show improved sample\nefficiency compared to the original action space from better alignment of the\naction space to the task space, as we observe with visualizations of the\nlearned action space manifold. Additional details:\nhttps://www.pair.toronto.edu/laser\n
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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 | 0.000 | 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 | 0.003 |
| 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".