Act 1507202660EP is a model for imitation learning in robotics that implements Action Chunking with Transformers (ACT). Below are 30 other ai apps with similar functionality to Act 1507202660EP, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
act_policy is an imitation-learning model for robotics that predicts short action chunks instead of single steps, improving success rates in manipulation tasks. It is compatible with LeRobot and supports research in robotic learning from demonstration.
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 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.
3cda2225/record-052_act_policy is an open-source policy model for robotics, trained with imitation learning to predict action chunks. It is designed for robotics researchers and developers working on advanced control and learning tasks, supporting local inference and open weights.
act_real190 is an open-source model for imitation learning in robotics, focusing on action chunking and policy inference. It enables robotics researchers to train and evaluate control policies using teleoperated data, supporting integration with robotics frameworks.
JosiahB5363/act_so101_piecetest715again is an open-source imitation-learning model for robotics, predicting short action chunks from teleoperated data. It helps researchers develop and evaluate robotic control policies using open weights and CLI tools.
act_DS1_1B1O_FF_removeidle_rand80_extendlf is a transformer-based imitation learning policy for robotics, predicting short action chunks from teleoperated data. It is open-source, supports API-based usage, and is designed for robotics researchers and engineers.
record-demo0714_2_act_policy is an open-source imitation learning model for robotics, implementing action chunking with transformers. It allows researchers and developers to train, evaluate, and deploy policies for robotic control using teleoperated data. The model is accessible via API and Python libraries.
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.
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.
Record Demo0714 4 Act Policy is a robotics policy model that implements action chunking with transformers. The model is designed for imitation learning, predicting short sequences of actions, or "action chunks," rather than individual steps. This approach is intended to improve performance by learning from teleoperated data and is reported to often achieve high success rates. The policy has been trained and made available through the Hugging Face Hub, and it can be used with the LeRobot library. Users are provided with instructions for employing the model with various tools, including libraries, inference providers, notebooks, and local applications. There are specific guides for using the model with LeRobot, as well as resources for running training and inference in environments such as Google Colab and Kaggle. 0 license. Documentation and training guides are referenced for users seeking a more detailed walkthrough on how to train the model from scratch or evaluate its policy and run inference. The tool is positioned within the class of robotics policy models, with a focus on action chunking and imitation learning methods.
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.
act_new_merge is an open-source imitation learning model for robotics, focused on action chunking from teleoperated data. It enables researchers and developers to deploy and experiment with advanced imitation learning techniques locally, supporting integration into robotics workflows.
Act Dataset Training is an imitation learning model that uses a method called Action Chunking with Transformers (ACT) to predict short sequences of actions, rather than individual steps. It is designed to learn from teleoperated data, with the goal of producing policies that can achieve high success rates in relevant tasks. The model is associated with robotics applications and has been trained and made available on the Hugging Face platform. This model can be trained from scratch and supports evaluation or inference on new data. Instructions are provided for using Act Dataset Training with various libraries, including LeRobot, as well as with platforms such as Google Colab and Kaggle. The model is distributed in the "safetensors" format and is accompanied by documentation and guides for training and running inference. Act Dataset Training is released under the Apache-2.0 license. It is accessible for use through the Hugging Face Hub, and its documentation references additional resources and guides for users interested in robotics and imitation learning research.
act_merged_dataset_pick_cube_new is an open-source imitation learning model for robotics, enabling action chunking from teleoperated data. It is suitable for researchers and developers working on robotic control and learning tasks.
Grasp Red Act C100 A100 50k is a robotics policy model available on Hugging Face. It implements Action Chunking with Transformers (ACT), an imitation-learning approach designed to predict short sequences of actions, referred to as action chunks, rather than single action steps. The model is trained using teleoperated data and is intended to facilitate robotic policy learning that can often achieve high success rates in its tasks. The model card indicates that Grasp Red Act C100 A100 50k can be used to run policies on robots and to train new policies. It is compatible with the LeRobot library, and instructions are provided for using the model with LeRobot, as well as in environments like Google Colab and Kaggle. The model is distributed in the Safetensors format and is available under the Apache-2.0 license. The evidence does not specify particular use cases, targeted user roles, or detailed deployment requirements beyond its compatibility with LeRobot and support for standard machine learning environments. No information is provided about pricing, beyond the open-source license, nor about specific integrations or supported robotics platforms. Further details on the model's features, training dataset, or evaluation metrics are not included in the provided evidence.
act_merged_dataset_pick_cube_new2 is an open-source robotics model for imitation learning and action chunking. It supports training and evaluation of robotic policies, aiding researchers in developing advanced robot control systems.
act_v3 is an open-source AI model for robotics, focusing on imitation learning and action chunking. It allows developers to integrate advanced policy learning into robotic systems using CLI tools and open weights.
act_dg5f_1.25sqblue_30 is an open-source imitation learning model for robotics, trained to predict action chunks from teleoperated data. It is distributed on Hugging Face and can be used with LeRobot and other libraries for research or deployment in robotics applications.
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_60 is an open-source robotics policy model based on imitation learning and action chunking. It enables robotics researchers to automate control tasks by predicting short action sequences from teleoperated data. The model is compatible with LeRobot and supports local inference.
act_dg5f_1.25sqblue_120 is an open-source model for robotics imitation learning, focusing on predicting short action chunks from teleoperated data. It is designed for researchers and developers working on robotics control and policy learning, and is distributed via Hugging Face with Apache-2.0 license.
act-record-test-test is an open-source imitation learning model for robotics, implementing action chunking with transformers. It enables developers to train and deploy policies that predict short action sequences from teleoperated data, improving robotic task performance.
kris0/act_v4 is an open-source imitation learning model designed for robotics, focusing on predicting short action chunks from teleoperated data. It supports integration with robotics libraries and can be used for both training and inference in research or practical robotics applications. The model is suitable for robotics researchers and developers.
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_merge_datasets0 is an open-source imitation-learning model for robotics, implementing action chunking with transformers. It is designed for robotics researchers and developers to train, evaluate, and deploy policies for sequential action prediction, and is available on Hugging Face.
act_policy_bimanual_20ep is an open-source robotics policy model for bimanual imitation learning, available on Hugging Face. It enables researchers and developers to train and evaluate bimanual robotic tasks using open weights and standard libraries.
act_merged_all24 is an open-source imitation learning model designed for robotics applications, enabling prediction of short action chunks from teleoperated data. It is intended for robotics researchers and developers who want to train, evaluate, or deploy advanced action chunking policies. The model is available on Hugging Face under an open license and integrates with libraries like LeRobot.
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.
camgap-act-v-w155-n500 is an open-source imitation learning model for robotics, implementing action chunking with transformers. It allows researchers to train, evaluate, and deploy policies that predict short action sequences, facilitating advanced robotics control and experimentation.
