Instructions to use Cache-SCA/Gr00t_BaseCaP_Sim_push_button_100ep_vision_encoder_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Cache-SCA/Gr00t_BaseCaP_Sim_push_button_100ep_vision_encoder_final with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
datasets: CoRL2026-CSI/IsaacLab-SO101_push_button_baseCaP_100epi_10fps
library_name: lerobot
license: apache-2.0
model_name: groot
pipeline_tag: robotics
tags:
- robotics
- groot
- lerobot
Model Card for groot
Model type not recognized — please update this template.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Model
For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
Train from scratch
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.
Evaluate the policy/run inference
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.
Model Details
- License: apache-2.0