Instructions to use iFaz/diffusion-pusht-seed3-half with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use iFaz/diffusion-pusht-seed3-half with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
datasets:
- lerobot/pusht
language: en
library_name: lerobot
license: apache-2.0
tags:
- robotics
- imitation-learning
- diffusion
- mujoco
- pytorch_model_hub_mixin
DIFFUSION Policy β diffusion_pusht_seed3
Trained with LeRobot.
Date: 2026-05-29 02:01
Policy type: diffusion | Device: cuda
π¦ Dataset
| Parameter | Value |
|---|---|
dataset.repo_id |
lerobot/pusht |
ποΈ Training Config
| Parameter | Value |
|---|---|
steps |
70000 |
batch_size |
8 |
eval_freq |
0 |
save_freq |
20000 |
num_workers |
4 |
seed |
3 |
eval.n_episodes |
1 |
eval.batch_size |
1 |
eval.use_async_envs |
True |
π Policy Architecture
| Parameter | Value |
|---|---|
noise_scheduler_type |
DDIM |
num_inference_steps |
15 |
π― Eval Config
| Parameter | Value |
|---|---|
env.type |
pusht |
env.task |
PushT-v0 |
eval.n_episodes |
8 |
eval.batch_size |
4 |
eval.use_async_envs |
False |
policy.path |
/kaggle/working/outputs/train/pusht_seed3/checkpoints/last/pretrained_model |
π Eval Results
| Metric | Value |
|---|---|
| Episodes | 8 |
| Success rate | 12.5% |
| Avg sum reward | 84.67 |
| Avg max reward | 0.66 |
| Eval time (s) | 48.2 |
Citation
@misc{cadene2024lerobot,
author = {Cadene, Remi and Alibert, Simon and others},
title = {LeRobot},
year = {2024},
url = {https://github.com/huggingface/lerobot}
}