NAJY_act_all11_c100_gist30_reef_sf_27D_60k_s1000

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

ACT policy trained to open a reagent container cap with the omxdual robot. This repository contains the final 100,000-step checkpoint, including normalization buffers in model.safetensors. - Excluded original Hugging Face episode indices after user visual review: 3, 35, 42, 48. - The retained episodes were copied and renumbered 0–44. See datasetselection.json for the original-to-subset correspondence. - Source data was preserved. All retained action/state values and videos were verified against the source. Provide all four image streams as [batch, 3, 480, 640] tensors in [0, 1], using the same camera views and preprocessing as the recordings: - observation.images.camhead…

Model · Robotics

cucumber_grasp_act_train_vibepi_20260922-v1-act

VibeCuisine

act policy trained via Vibe Data Studio on 20 teleop episodes. Language-conditioned — pass one of these instructions at inference: - Action/state (7-dim): shoulderpan, shoulderlift, elbowflex, wristflex, wristroll, gripper, tilt - Cameras @ 20 fps: base 640×480, top 640×480, wrist 640×480 - Consumed image inputs (the checkpoint's resolved VISUAL features, from its config.json): observation.images.base, observation.images.top, observation.images.wrist Materialized collection cucumbergraspacttrainvibepi20260922@v1 (frozen 2026-09-23): - autoresearch-vibepi-000655-task-cucumbergrasp @ d55dfe57 (1 eps) - autoresearch-vibepi-000656-task-cucumbergrasp @ ffba74e6 (1 eps)…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…