act_dg5f_1.25sqblue_60 is an open-source robotics policy model based on imitation learning and action chunking. Below are 23 other ai apps with similar functionality to Act Dg5f 1.25sqblue 60, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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_dg5f_blue_1.25sq2_60 is an open-source imitation learning model for robotics, trained to predict action chunks from teleoperated data. It allows robotics researchers to run, evaluate, and integrate advanced action chunking policies into their own systems using local inference.
act_dg5f_blue_1.25sq2_30 is an open-source robotics policy model based on imitation learning, designed for use with the LeRobot library and other robotics frameworks. It enables researchers to train, evaluate, and deploy action chunking policies for robotic arms and similar platforms. The model is distributed under an open license for local inference and further research.
25sq2 90 is a machine learning model designed for robotics applications, specifically utilizing an approach known as Action Chunking with Transformers (ACT). This method focuses on predicting short sequences of actions, referred to as action chunks, rather than individual steps. The model is trained using teleoperated data, enabling it to learn from demonstrations and often achieve high success rates in policy execution. The model has been trained and made available through the Hugging Face Hub, with instructions for use provided for integration with libraries, inference providers, notebooks, and local applications. LeRobot is specifically mentioned as a compatible library for using and training the model, and users are directed to additional documentation and guides for a complete walkthrough of the training and inference process. 0 license. 25sq2 90 is relevant for those working in robotics who seek to implement or evaluate imitation-learning policies using action chunking techniques. The model card and associated resources support both training from scratch and evaluating or running inference with the trained policy.
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_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 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.
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_DS1_1B1O_FF_removeidle_rand20 is an open-source robotics policy model for action chunking in manipulation tasks, built with imitation learning. It is designed for robotics researchers and integrates with LeRobot for deployment.
act_DS1_1B1O_FF_removeidle_rand60 is an open-source imitation learning model for robotics, trained to predict action chunks from teleoperated data. It is designed for robotics researchers seeking to implement advanced policy learning and action chunking in robotic systems.
Act DS3 1B1O FV Removeidle Rand80 is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation learning approach for robotics. Unlike methods that predict single action steps, this model is designed to predict short sequences of actions, referred to as action chunks. According to the provided information, it is trained using teleoperated data and aims to achieve high success rates in its tasks. The model is associated with the LeRobot library and can be used in conjunction with platforms such as Google Colab and Kaggle. It is distributed in the Safetensors format, and users are provided with instructions for using the model with various libraries, inference providers, notebooks, and local applications. The evidence also references an arXiv paper (arxiv: 2304.13705) for further context on the method, though no details from the paper are included in the excerpt. Act DS3 1B1O FV Removeidle Rand80 is released under the Apache 2.0 license, making it available for open-source use. The model is suitable for those interested in robotics and imitation learning, particularly in scenarios where learning from teleoperated demonstrations is required. Further documentation and guides are mentioned as available through the LeRobot documentation and training guide.
act_DS1_1B1O_FF_removeidle_rand40 is an open-source imitation learning model designed for robotics applications. It predicts short action chunks from teleoperated data, enabling more efficient and robust policy learning. The model integrates with LeRobot and is suitable for robotics researchers and developers.
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_DS3_1B1O_FV_removeidle_rand40 is an open-source imitation learning model for robotics, focusing on action chunking using transformer architectures. It is designed for robotics researchers and developers working on teleoperated data and policy learning.
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.
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.
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.
act_DS1_1B1O_FF_removeidle_rand80 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.
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_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_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.
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.
