act_policy_test4 is an open-source imitation-learning policy model designed for robotics applications, focusing on predicting short action chunks from teleoperated data. Below are 22 other ai apps with similar functionality to Act Policy Test4, 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 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_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.
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_policy_so101_cube_multitask_0710 is an open-source robotics policy model for multitask cube manipulation, leveraging imitation learning and transformer architectures. It integrates with LeRobot and is intended for robotics researchers and developers.
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.
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.
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.
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-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_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.
omx-test-1-policy is an open-source robotics policy model that uses action chunking with transformers to improve control in robotics applications. It integrates with LeRobot and is distributed under the Apache-2.0 license via Hugging Face.
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.
record-demo0714_3_act_policy is an open-source imitation learning policy model for robotics, trained using the LeRobot framework. It predicts action chunks for robotic control and is intended for researchers and developers working on robotic automation and learning from demonstration.
Task1 is a robotics policy model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation learning approach designed to predict short sequences of actions, or action chunks, rather than individual steps. The model is trained using teleoperated data and is intended to achieve high success rates in robotic control tasks. Task1 has been trained and uploaded to the Hugging Face Hub, where it can be accessed and used with various libraries, including LeRobot, as well as through platforms such as Google Colab and Kaggle. The model is suitable for use with robots of type 'so_follower' and is compatible with side and window-side camera inputs. The model card provides information about the model’s inputs and outputs, training dataset, and configuration, as well as instructions for running the policy on a robot or training a custom policy. Task1 is distributed under the Apache-2.0 license, making it available for open-source use. The evidence does not specify further details about its intended audience or additional features beyond those mentioned.
pick-the-1nut-dataset_act-policy-v1 is an open-source robotics policy model for action chunking and imitation learning. It is designed for robotics researchers and integrates with LeRobot for training and deployment in control tasks.
act_pick_place_policy is an open-source imitation learning model for robotics, focused on automating pick-and-place tasks. It enables researchers and developers to implement and experiment with advanced robotic control policies.
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.
topright-act is a transformer-based imitation-learning policy model for robotics, focused on action chunking. It allows robotics researchers and developers to implement and experiment with advanced policy learning techniques using open-source weights and documentation.
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.
pick-the-various-direction-bolt-dataset_act-policy-v1 is an open-source robotics policy model based on imitation learning and action chunking with transformers. It allows robots to learn complex behaviors from teleoperated demonstrations and is compatible with LeRobot and other robotics frameworks. Distributed under the Apache-2.0 license.
act_pick_place_v2 is an open-source robotics policy model based on imitation learning and transformers, designed for automating pick-and-place tasks. It integrates with LeRobot and is suitable for robotics researchers and developers seeking pretrained action chunking models.
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.
