act_merged_all24 is an open-source imitation learning model designed for robotics applications, enabling prediction of short action chunks from teleoperated data. Below are 10 other ai apps with similar functionality to Act Merged All24, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
act_task1_v2 is an open-source imitation learning policy for robotics, trained to predict action chunks from teleoperated data. It is designed for use with LeRobot and supports local inference and further training by robotics researchers and engineers.
act_ego_1kitkat_hand_merge3_colabtest is an open-source robotics policy model for imitation learning and action chunking. It is designed for use with LeRobot and supports local inference for robotics research and development.
act_subtask_g2 is an open-source model for imitation learning in robotics, focusing on predicting and executing short action chunks. It is intended for robotics researchers and developers who need to train or evaluate robotic policies using teleoperated data.
act-hyak-test is an open-source imitation learning model for robotics, designed to predict action chunks from teleoperated data. It enables researchers and developers to run local inference and integrate advanced policy models into robotics workflows.
act_DS3_1B1O_FV_removeidle_rand20 is an open-source model for robotic action chunking using imitation learning. It enables robotics researchers to implement and evaluate action chunking policies for efficient robot control.
kob0105/act_pusht is an open-source imitation-learning model for robotics, designed to predict short action chunks from teleoperated data. It is available on Hugging Face for use in research and development, supporting integration with LeRobot and Colab.
act_dg5f_1.25sqblue_90 is an open-source imitation learning model designed for robotics applications, enabling prediction of short action chunks from teleoperated data. It is compatible with LeRobot and can be used for training, evaluation, and inference in robotics workflows. The model is suitable for robotics researchers and developers seeking advanced action chunking capabilities.
Act Aloha Test is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation learning approach for robotics. Rather than predicting individual action steps, this method predicts short sequences of actions, or 'action chunks', which are learned from teleoperated data. The model aims to improve the performance of robotics policies by focusing on these action chunks, and it has been trained and published using the LeRobot library. The tool is designed for users interested in robotics and imitation learning, particularly those who wish to train or evaluate policies that benefit from chunked action prediction. Instructions are provided for training the model from scratch and running inference or evaluation. The model can be used with libraries such as LeRobot, and there are resources for integrating it with platforms like Google Colab and Kaggle. 0 license, making it open source. Documentation and guides for using, training, and evaluating the model are available through linked resources. The model and its associated files can be accessed and utilized through the Hugging Face platform.
Act Policy is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation learning approach designed to predict short sequences of actions, referred to as action chunks, rather than individual steps. The method is trained using teleoperated data, with the goal of achieving high success rates in robotic tasks. According to the evidence, this policy has been trained and uploaded to the Hugging Face Hub using LeRobot, a named library that can be used to train and run the model. The referenced robot type for this policy is 'omx_follower,' and it utilizes input from a camera labeled 'camera2.' The model is distributed under the Apache 2.0 license, making it open source. Users are provided with instructions for integrating the model with libraries, inference providers, notebooks, and local applications, with specific mentions of Google Colab and Kaggle as platforms where the model can be used. The evidence also points to documentation and guides for training and deploying the policy with LeRobot. No additional details about broader compatibility, pricing beyond the open-source license, or intended audience are explicitly stated in the evidence.
act_policy_test4 is an open-source imitation-learning policy model designed for robotics applications, focusing on predicting short action chunks from teleoperated data. It can be integrated with LeRobot and other frameworks for training, evaluation, and deployment in robotics workflows. Ideal for robotics researchers and developers seeking ready-to-use policy models.
