pick_red_cube_act is an open-source imitation learning model for robotics, trained to predict action chunks from teleoperated data. Below are 24 other ai apps with similar functionality to Pick Red Cube Act, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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.
so101_pick_cube_v2_act is an open-source robotics model implementing action chunking with transformers for imitation learning. It predicts short action sequences for robotic manipulation, enabling efficient policy inference and training. Designed for robotics researchers and developers, it integrates with LeRobot and supports evaluation and further training.
Pick1 Jenga1 Act is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning method for robotics. Rather than predicting individual action steps, ACT predicts short sequences of actions, referred to as action chunks. The model is trained using teleoperated data and is designed to enable policy inference and evaluation in robotics tasks. According to the evidence, Pick1 Jenga1 Act has been trained and uploaded to the Hugging Face Hub using LeRobot, and it can be used with various libraries, inference providers, notebooks, and local applications. The tool provides instructions for use with LeRobot, as well as compatibility with Google Colab and Kaggle notebooks. The model card mentions that users can train the model from scratch or evaluate its policy and run inference. Pick1 Jenga1 Act is released under the Apache 2.0 license. The model is intended for those working in robotics, particularly in contexts where learning from demonstration or teleoperation data is relevant. The documentation and guides referenced in the evidence provide resources for getting started with training and evaluation, but no further details about specific features, supported robotics platforms, or integration capabilities are provided. Pricing information is not mentioned, but the Apache 2.0 license indicates it is open source. Overall, Pick1 Jenga1 Act serves as an open-source imitation-learning model for robotics, focusing on predicting sequences of actions from teleoperated data, and is accessible through the Hugging Face platform.
act_pickplace_test is an open-source imitation learning model designed for robotic pick-and-place tasks. It leverages action chunking with transformers to predict and execute short action sequences, making it valuable for robotics researchers and developers working on automation and manipulation tasks.
act_pick_place is an open-source imitation learning model designed for robotic pick-and-place operations. It integrates with LeRobot and similar robotics frameworks, enabling engineers to deploy and evaluate action chunking policies for manipulation tasks. The model is suitable for research and practical robotics applications.
act_strawberry_pick_v2 is an open-source imitation learning model designed for robotics applications, specifically for predicting and executing short action chunks. It is available on Hugging Face for use with local inference or integration into robotics pipelines, targeting researchers and developers in robotics and AI.
Cube To Cup Act is a robotics policy model based on imitation learning, designed to predict short sequences of actions—referred to as action chunks—rather than single-step actions. This approach enables the model to learn from teleoperated data and is intended to achieve high success rates in robotic tasks. The model has been trained and made available through the Hugging Face Hub, and it is associated with the LeRobot library, which provides guidance on training and deploying the policy. The tool is delivered as a model that can be used with various libraries, inference providers, notebooks, and local applications. Specific instructions are provided for integration with LeRobot, and there are resources for running the policy on robots, training custom policies, and evaluating performance. The model card and documentation offer further details on usage and implementation. 0 license, making it available as open-source software. It is positioned within the field of robotics and imitation learning, with a focus on action chunking using transformer-based methods.
ACT Real PickOrange is a model available on Hugging Face that provides an ACT policy for real-world robotic pick-and-place tasks, specifically for the SO-101 pick-orange scenario. The model is developed using a process where training begins in simulation, using the Isaac Lab environment (LeIsaac-SO101-PickOrange-v0), to achieve the best performance on a simulation leaderboard. This checkpoint is then used as a starting point (sim-warm-start) and further fine-tuned on real hardware through 30 teleoperation episodes, amounting to 5000 additional training steps. The result is a policy intended for manipulation tasks with real robotic arms, focusing on the challenge of picking oranges in a physical setting. The model is distributed under the Apache 2.0 license. Instructions for using the model with libraries such as LeRobot, as well as with platforms like Google Colab and Kaggle, are provided. No further details about intended users, broader capabilities, or additional features are specified in the available evidence.
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.
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_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.
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.
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.
jihwanooh/act-realm-pen-pickup is an open-source imitation learning policy for robotics, enabling researchers and developers to implement and test action chunking for manipulation tasks. It supports local inference and is suitable for robotics experimentation.
grasp_redblock_act_c100_a100_50k_v2 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_pick_bottle is an open-source imitation-learning model for robotics, trained to predict action chunks for manipulation tasks. It integrates with LeRobot and is suitable for researchers developing robotic control systems.
Pick_up_Panadol_act_20260715_112152 is an open-source model for robotics imitation learning, enabling robots to learn and execute action chunks from teleoperated demonstrations. It is designed for robotics researchers and developers.
pick-the-1washer-dataset_act-policy-v1 is an open-source imitation-learning model for robotics, focusing on action chunking from teleoperated data. It is designed for researchers and developers to train, evaluate, and deploy advanced robotic control policies.
Pick_up_Panadol_act_20260715_080936 is an open-source imitation-learning model for robotics, trained to predict short action chunks from teleoperated data. It is designed for use with LeRobot and similar frameworks, enabling developers to implement advanced action policies in robotics applications.
act_white_in_blue_circle is an open-source imitation learning model that uses transformers to predict action chunks for robotic control. It is designed for robotics researchers and developers seeking advanced methods for efficient robot learning and manipulation.
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.
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.
Pick Ee, also referred to as π₀.₅ (Pi05), is a vision-language-action model designed for robotics applications, with a particular focus on enabling open-world generalization. The model aims to address the challenge of allowing robots to operate effectively in environments and situations that were not encountered during training, moving beyond controlled settings. Pick Ee is associated with Physical Intelligence and represents an evolution from a previous model, π₀, to further improve generalization capabilities in robotics tasks. The implementation of Pick Ee on Hugging Face references integration with LeRobot, and it is adapted from the open source OpenPI repository. The model is available for use with various tools and platforms, including Google Colab and Kaggle, as indicated by the provided instructions for getting started with the model in notebooks and local applications. The model card and associated files are accessible through the Hugging Face platform. Pick Ee is distributed under the Apache 2.0 license. The evidence does not specify details about pricing, but the presence of an open-source license suggests that the model is available for use under those terms. The evidence does not provide further information about specific features, user roles, or additional integrations beyond those named above.
