Lebruhbruh/sarm-charging-bimanual

Reward Model Card for sarm

<!-- Provide a quick summary of what the reward model is/does. -->

A Success-Aware Reward Model (SARM) predicts a dense reward signal from observations, typically used downstream for reinforcement learning or human-in-the-loop fine-tuning when task success is not directly observable.

This reward model has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.

How to Get Started with the Reward Model

Train from scratch

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --reward_model.type=sarm \
  --output_dir=outputs/train/<desired_reward_model_repo_id> \
  --job_name=lerobot_reward_training \
  --reward_model.device=cuda \
  --reward_model.repo_id=${HF_USER}/<desired_reward_model_repo_id> \
  --wandb.enable=true

Writes checkpoints to `outputs/train/<desiredrewardmodelrepoid>/checkpoints/`.

Load the reward model in Python

from lerobot.rewards import make_reward_model

reward_model = make_reward_model(pretrained_path="<hf_user>/<reward_model_repo_id>")
reward = reward_model.compute_reward(batch)

Model Details

• —License: apache-2.0