Deep networks for few-shot manipulation learning from scratch
Bibliographic record
Abstract
Deep networks can learn to process raw sensor data and produce control output for diverse tasks. However, to leverage these models’ flexibility and expressive power, past studies have trained them on massive amounts of data. In contrast, in this work, we attempt to train deep networks from scratch with very small datasets of object pose and gripper trajectories in manipulation-task demonstrations. The same setting has previously been used in programming-by-demonstration work with specialized statistical models such as task-parameterized Gaussian mixture models (TP-GMMs). We show that deep networks can learn manipulation tasks with performance that meets or exceeds that of past statistical models, given the same small numbers of demonstrations (5-30 in our tests), without any pretraining. Data augmentation is important for good performance and training the deep networks to be equivariant to frame transformations. Transformers performed slightly better than parameter-matched long-short-term-memory (LSTM) networks, and transformers had better training and inference times. In addition to testing these methods with physical tasks, we used a family of synthetic tasks to show that larger transformer models exhibit positive transfer across dozens of tasks, performing better on each task as they are trained on others. These results suggest that deep networks are potential alternatives to TP-GMM and related methods, having the advantage of needing fewer examples per task as the number of tasks grows. The results also suggest that the large data requirements of end-to-end manipulation learning are mainly due to perceptual factors, which may help to improve the design of end-to-end systems in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".