https://vimeo.com/983024799?autoplay=1&loop=1&autopause=0&background=1&muted=1&ratio=1920:1080 ## Background LeRobot is a project by huggingface that aims to provide models, datasets and tools for real-world robotics in PyTorch. This example shows how one can train a model on the [pusht-dataset](https://huggingface.co/datAsets/lerobot/pusht) and visualize it's progress using rerun. ## Run the code This is an external example, check the [repository](https://github.com/rerun-io/lerobot/tree/alexander/train_viz) for more information. To train the model as shown in the video, install git-lfs and clone the [repository](https://github.com/rerun-io/lerobot/tree/alexander/train_viz) and then run the following code: ``` pip install -e '.[pusht]' WANDB_MODE=offline python lerobot/scripts/train.py \ hydra.run.dir=outputs/train/diffusion_pusht \ hydra.job.name=diffusion_pusht \ policy=diffusion \ env=pusht \ env.task=PushT-v0 \ dataset_repo_id=lerobot/pusht \ training.offline_steps=20000 \ training.save_freq=5000 ++training.log_freq=50 \ training.eval_freq=1500 \ eval.n_episodes=50 \ wandb.enable=true \ wandb.disable_artifact=true \ device=cuda ``` If you don't have CUDA installed you will have to change the last argument `device=cuda` to `device=cpu` or another device.