bjb7 / touch_the_eraser_reward_classifier

huggingface.co
Total runs: 12
24-hour runs: 0
7-day runs: -4
30-day runs: -26
Model's Last Updated: December 03 2025
robotics

Introduction of touch_the_eraser_reward_classifier

Model Details of touch_the_eraser_reward_classifier

Model Card for reward_classifier

A reward classifier is a lightweight neural network that scores observations or trajectories for task success, providing a learned reward signal or offline evaluation when explicit rewards are unavailable.

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

Runs of bjb7 touch_the_eraser_reward_classifier on huggingface.co

12
Total runs
0
24-hour runs
-2
3-day runs
-4
7-day runs
-26
30-day runs

More Information About touch_the_eraser_reward_classifier huggingface.co Model

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touch_the_eraser_reward_classifier install

touch_the_eraser_reward_classifier is an open source model from GitHub that offers a free installation service, and any user can find touch_the_eraser_reward_classifier on GitHub to install. At the same time, huggingface.co provides the effect of touch_the_eraser_reward_classifier install, users can directly use touch_the_eraser_reward_classifier installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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