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. Below are 29 other ai apps with similar functionality to Act Dataset Training, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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_ImproveDatasetMerge is an open-source imitation-learning model that predicts short action chunks instead of single steps, trained on teleoperated data. It is designed for robotics and AI researchers to automate and improve action sequence prediction. The model supports training, evaluation, and fine-tuning for custom tasks.
act_UPDATED_DATASET 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 Dataset Assessment2 is a model focused on robotics, specifically implementing an imitation-learning approach called Action Chunking with Transformers (ACT). Rather than predicting individual actions, this method forecasts short sequences of actions, known as action chunks, based on teleoperated data. The model aims to improve policy performance by leveraging these action chunks, which can lead to higher success rates in certain robotics tasks. The model has been trained and made available through the Hugging Face platform and is associated with the LeRobot library. Users can integrate Act Dataset Assessment2 with LeRobot, and there are instructions for using the model with various tools, including Google Colab and Kaggle notebooks. The model is distributed in the safetensors format, which is commonly used for secure and efficient model storage. 0 license, allowing for open-source use and modification. Documentation and guides are available to help users train the model from scratch, evaluate its policy, or run inference. The model card and further documentation can be accessed through the LeRobot Docs, providing additional resources for those interested in deploying or further developing the model. evidence_sufficient": true}
00Aswad/act_combine_dataset is an open-source imitation-learning model for robotics, designed to predict action chunks from teleoperated data. It is intended for robotics researchers and developers using LeRobot or similar frameworks.
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.
act_train_improve_merge is an open-source model for robotics that implements imitation learning with action chunking. It allows researchers to train, evaluate, and improve robotic policies using teleoperated data, supporting integration with robotics libraries and local experimentation.
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 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_revised_dataset is an open-source imitation learning policy model for robotics, distributed via Hugging Face. It enables researchers to train, evaluate, and deploy action chunking policies using LeRobot and other frameworks. The model is suitable for robotics and AI research applications.
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.
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_dryrun is an open-source imitation learning model for robotics, enabling prediction of action chunks from teleoperated data. It is designed for robotics researchers and developers to train, evaluate, and deploy advanced policies using the LeRobot library. The model is available on Hugging Face under an open license.
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_cranex7_multisensor_20260717_125016 is an open-source transformer-based imitation learning model designed for robotics applications. It predicts short action chunks from teleoperated data, enabling automation of complex robotic tasks. The model is suitable for robotics researchers and developers seeking to implement advanced policy learning in their projects.
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_RealDataMerge_finetuned_clawdataset is an open-source robotics imitation-learning model for action chunking, trained on teleoperated data. It is available via API and CLI on Hugging Face and is intended for robotics researchers and developers working on policy learning and automation. The model is distributed under an open license.
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.
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 1507202660EP is a model for imitation learning in robotics that implements Action Chunking with Transformers (ACT). Unlike approaches that predict single steps, this method predicts short action chunks, allowing it to learn from teleoperated data. The model is designed to achieve high success rates in its tasks by leveraging this chunk-based prediction strategy. It has been trained and made available through the Hugging Face Hub, and users can interact with it using various libraries and platforms, including LeRobot. The documentation references support for running the model in environments such as Google Colab and Kaggle, and provides instructions for training from scratch as well as for running inference and evaluation. The model card and training guide are available for those seeking a more detailed walkthrough of its capabilities and usage. Act 1507202660EP is released under the Apache-2.0 license, making it available for open-source use. The model is particularly relevant for those working in robotics who are interested in imitation learning techniques that utilize teleoperated data for policy training and evaluation.
act_slow is an open-source robotics model that implements action chunking with transformers for imitation learning. It is designed for robotics researchers and developers who want to train and deploy advanced policies for robotic control tasks.
act_DS3_1B1O_FV_removeidle_rand60 is an open-source transformer-based imitation learning model for robotics. It predicts action chunks from teleoperated data, helping researchers and developers build advanced robotic control systems.
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_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.
This Hugging Face model provides an imitation-learning policy for robotics, predicting action chunks from teleoperated data. It is open source and can be integrated into robotics workflows via API or CLI, supporting research and development in robotics automation.
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-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.
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-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.
