so101-button-touching-policy-ideal-30 is an open-source imitation learning policy for robotics, trained to predict action chunks from teleoperated data. Below are 6 other ai apps with similar functionality to So101 Button Touching Policy Ideal 30, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
so101-button-touching-policy-ideal-25 is an open-source robotics policy model for button-touching tasks, designed for use with the LeRobot library. It leverages imitation learning and action chunking to improve task performance, supporting robotics research and development.
so101_train_ACT_2cam_PCLab_100ep_khoa_policy is an open-source imitation-learning policy model for robotics, trained to predict action chunks from teleoperated data. It is intended for robotics researchers and developers using the LeRobot framework.
so101_pick_cube_v2_act is an open-source robotics model implementing action chunking with transformers for imitation learning. It predicts short action sequences for robotic manipulation, enabling efficient policy inference and training. Designed for robotics researchers and developers, it integrates with LeRobot and supports evaluation and further training.
So101-act-test is an open-source robotics model implementing action chunking with transformers for imitation learning. It predicts short action sequences from teleoperated data, supporting robotics research and development. Distributed via Hugging Face with open weights and documentation for integration.
act_so101_test is an open-source imitation-learning model for robotics, focusing on action chunking and policy inference. It is designed for robotics researchers and developers to train and evaluate robotic policies using open weights and Python integration.
act_so101_cube_left_right_v2 is an open-source imitation-learning model for robotics, designed to predict and execute action chunks based on teleoperated data. It supports integration with robotics frameworks and is suitable for researchers and developers working on robotic control and policy learning.
