| --- |
| tags: |
| - robotics |
| - imitation-learning |
| - pusht |
| - diffusion-policy |
| --- |
| # PushT dit, seed 0 |
|
|
| Final EMA policy trained from scratch for 512,400 optimizer updates. |
| RGB96 + agent XY, two observations, 16 prediction / eight execution steps. |
| Original lossless DP PushT archive; original 90-demo split; effective batch64 |
| (final epoch batch may be smaller), AdamW1e-4, cosine500 warmup, warmed EMA. |
| The final checkpoint is published regardless of test score; it is not test-selected. |
|
|
| ## Method and reproduction scope |
|
|
| - RGB PushT controlled reproduction; original DP 90-demo split and normalization, fixed 512400 optimizer updates at effective batch64. |
| - TDP is derived from the paper, as authors have not released code or complete PushT hyperparameters. Tube constants and action-to-U-Net lift are explicit implementation choices. |
| - GRN is an action-domain adaptation, with a larger configurable temporal head. |
| - DiT uses Dasari et al. DiT-Block conditioning adapted to shared PushT RGB encoder, not a pretrained VLA. |
| - Modern gym-pusht preserves legacy state reset order but is not the original archived DP environment; version recorded. |
| - Periodic checkpoint evaluation reports test score as in the paper; final published policy is the final EMA, not the best test-selected checkpoint. |
|
|
| TDP settings not supplied by its authors are explicit in config.json, including |
| contraction/noise constants, continuous interpolation, and single-action U-Net lift. |
| This controlled RGB comparison does not assert reproduction of reported paper scores. |
| Evaluation JSON reports mean maximum reward separately from strict success rate. |
|
|
| ## Load |
|
|
| Install requirements.txt using a CUDA-compatible PyTorch/torchvision pair, extract |
| inference_source.tar.gz, and run: |
| |
| ```python |
| from pusht_parallel.inference import load_policy, PushTController |
| model, payload = load_policy("policy.pt", device="cuda") |
| controller = PushTController(model, payload, device="cuda", nfe=10) |
| action_xy = controller(observation) # {"pixels": uint8[96,96,3], "agent_pos": float[2]} |
| ``` |
| |
| Call controller.reset() before each episode. Model outputs are XY position commands. |
| |
| Sources: [Tube DP](https://arxiv.org/abs/2604.23609), |
| [original DP experiment](https://diffusion-policy.cs.columbia.edu/data/experiments/image/pusht/diffusion_policy_cnn/config.yaml), |
| [DiT-Block Policy](https://github.com/SudeepDasari/dit-policy), |
| [GRN](https://github.com/bytedance/GRN). |
| |
| W&B: https://wandb.ai/yuyuan-wu-universiteit-gent/policy%20parallel%20test/runs/pusht-parallel-20261019-s0-dit-s0 |
| |
