Robotic Object Manipulation with Full-Trajectory GAN-Based Imitation Learning
Bibliographic record
Abstract
This paper develops a novel generative imitation learning system capable of capturing the distribution of expert demonstrations in trajectory space, which allows longer temporal context within complex motion sequences to be captured. While auto-regressive models that model time-steps sequentially can in principle be recursively applied to capture long sequences, there are known issues with learning such models reliably. In contrast, our model represents full trajectories a first-class entities, which has required us to adapt the typical generative adversarial learning architecture. We pair a full-trajectory discriminator with an imitation-inspired generative trajectory model and train these two in adversarial fashion. Our results show that our method matches the performance of existing approaches for simple tasks, in simulation and on real robot deployments. We produce state-of-the-art accuracy in replicating motions that contain long-term dependencies such as pouring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".