cube-to-cup-act is an open-source imitation learning policy for robotics, implementing action chunking with transformers. Below are 14 other ai apps with similar functionality to Cube To Cup Act, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
ball_cup_act_camera_inverted_model is an open-source transformer-based imitation learning model designed for robotics applications. It predicts short action chunks from teleoperated data, enabling robots to perform complex tasks efficiently. The model is intended for robotics researchers and developers working on advanced robotic control.
ball_cup_act_hil_round1_model is an open-source imitation learning model for robotics, designed for action chunking tasks. It is distributed on Hugging Face and can be used via API or CLI, supporting research and development in robotic control and automation.
camgap-act-ch-act-n500 is an open-source transformer-based imitation learning model for robotics, designed to predict short action chunks from teleoperated data. It helps researchers develop more efficient and robust robotic control policies using chunked actions.
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.
gact-cubes-32a is an open-source robotics policy model designed for gaze-based action control, distributed on Hugging Face. It is intended for robotics developers who need pretrained models for gaze-actuation tasks and integrates with LeRobot and other robotics libraries. The model is available under the Apache 2.0 license and supports local inference.
camgap-act-sa-abs-n500 is an open-source imitation learning model for robotics, focusing on action chunking and policy evaluation. It is designed for researchers and developers working on robotics control and learning, and can be used via CLI or API.
camgap-act-rf-rel-fast-n500 is an open-source imitation learning policy for robotics, implementing action chunking with transformers. Distributed via Hugging Face, it allows researchers and developers to train, evaluate, and deploy robotic policies for manipulation tasks. The model supports integration with robotics frameworks and fine-tuning for custom use cases.
This Hugging Face repository offers an open-source robotics policy model designed for use in robotics research and development. The model can be integrated with Python and supports local inference, making it suitable for custom robotics applications. Distributed under the Apache-2.0 license.
camgap-act-v2-n500 is an open-source transformer-based imitation-learning model for robotics, designed to predict short action chunks from teleoperated data. It is intended for robotics researchers and developers working on advanced control policies.
act_so101_pick_cube_v2 is an open-source imitation learning model designed for robotics applications. It predicts short action chunks for robots, enabling them to perform complex tasks by learning from teleoperated demonstrations. The model is available on Hugging Face under an Apache-2.0 license and can be integrated with robotics libraries such as LeRobot. It is intended for robotics researchers and developers seeking advanced policy models for robot control.
Act So101 Pick Cube is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning approach for robotics. The ACT method predicts short sequences of actions, referred to as action chunks, rather than individual steps. This technique is designed to learn from teleoperated data, enabling the model to achieve high success rates in robotic tasks. The policy represented by Act So101 Pick Cube has been trained and published using the LeRobot platform, and users are provided with instructions for utilizing the model through various libraries, inference providers, notebooks, and local applications. The documentation points to resources for running the policy on a robot or training a custom policy, with support for platforms such as Google Colab and Kaggle. The model is licensed under the Apache 2.0 license. The evidence also notes that the model is associated with a specific robot type, labeled as so_f. Further technical details, such as the training dataset and configuration, are referenced in the model documentation and linked arXiv paper. No information is provided about pricing, user roles, or specific deployment requirements beyond the platforms and libraries named.
Camgap Act C Act N500 is a model that implements Action Chunking with Transformers (ACT), an imitation learning approach designed to predict short sequences of actions rather than individual steps. The model is trained on teleoperated data, with the goal of improving the success rate of learned policies in robotics tasks. According to the available evidence, this policy has been trained and made accessible through the Hugging Face Hub, and it can be used in conjunction with the LeRobot library. Instructions are provided for using the model with libraries, inference providers, notebooks, and local applications, including platforms such as Google Colab and Kaggle. The model is positioned within the field of robotics and is relevant for tasks that benefit from action chunking, where predicting groups of actions can be more effective than single-step predictions. The model card and documentation reference a training guide for those interested in training from scratch or evaluating policy performance. The licensing for Camgap Act C Act N500 is specified as Apache-2.0, indicating that it is open source and can be freely used under the terms of that license. While the evidence highlights the model's integration with LeRobot and its focus on imitation learning from teleoperated data, it does not provide further details about specific use cases, supported robotics platforms, or additional integrations beyond those mentioned. The tool is intended for those working in robotics, particularly in research or development contexts where learning effective action policies from demonstration is important. The model and its associated resources are delivered via the Hugging Face platform, with usage instructions available for various environments.
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.
Camgap Act A Act N500 is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning method designed to predict short action chunks rather than single steps. The model is trained from teleoperated data, which enables it to learn sequences of actions, and it is noted for often achieving high success rates in its tasks. This policy has been trained and uploaded using LeRobot, and instructions are provided for using the model with various libraries, inference providers, notebooks, and local applications. The model can be trained from scratch or used for inference and evaluation, with guidance available for both processes. It is made available under the Apache 2.0 license. The model is relevant for robotics applications, as indicated by its association with the Robotics and LeRobot tags. Further documentation and a training guide are referenced for users seeking a complete walkthrough of its use. No additional details about specific integrations, user roles, or pricing beyond the open-source license are provided in the available evidence.
