Act So101 Test Alternatives

So101-act-test is an open-source robotics model implementing action chunking with transformers for imitation learning. It predicts short action sequences from teleoperated data, supporting robotics research and development. Distributed via Hugging Face with open weights and documentation for integration.

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_so101_grasp_50_v2 is an open-source robotics model implementing Action Chunking with Transformers (ACT) for imitation learning. It predicts short action chunks from teleoperated data, helping robotics researchers and developers train and evaluate robotic policies efficiently. The model is available for use via Hugging Face and integrates with tools like LeRobot.

heongyu/act_so101_t1_v2 is a model hosted on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning approach designed for robotics. The method focuses on predicting short sequences of actions, called action chunks, rather than individual steps. This enables the model to learn from teleoperated data and, as described, often results in high success rates for the tasks it is trained on. The model is associated with the LeRobot library, which provides instructions for using, training, and evaluating the policy. Users can train the model from scratch, evaluate its performance, or run inference, with further guidance available through linked documentation and training guides. The model card references an academic paper (arxiv: 2304.13705) for those seeking additional technical details. heongyu/act_so101_t1_v2 is distributed under the Apache 2.0 license, allowing for open-source use and modification. The model is delivered through the Hugging Face platform, with support for integration into workflows using libraries, inference providers, notebooks, and local applications. Specific instructions are available for use with platforms such as Google Colab and Kaggle. This tool is intended for applications in robotics, particularly where imitation learning from teleoperated demonstrations is relevant. The evidence does not specify particular user roles or industries beyond this context. Pricing information is not mentioned, but the Apache 2.0 license indicates it is available for open-source use. No further details about integrations, user interface, or additional features are provided.

Act So101 2nd is a model designed for robotics applications, specifically implementing Action Chunking with Transformers (ACT), an imitation-learning method. Rather than predicting individual robotic actions step by step, this approach focuses on forecasting short sequences of actions, or action chunks, based on teleoperated data. The model is trained to learn from demonstrations, aiming to achieve high success rates in its tasks. It is associated with the so_follower robot type and utilizes cameras as part of its input configuration. The model is available on the Hugging Face platform and can be integrated with LeRobot, a library referenced for both training and running the policy. Instructions are provided for using the model with various tools, including notebooks like Google Colab and Kaggle. The model card and documentation offer guidance on how to deploy and train the policy, as well as evaluation procedures. Act So101 2nd is distributed under the Apache-2.0 license, which permits open-source use and modification. No further details about specific features, intended user roles, or pricing structures beyond the open-source license are provided in the available evidence.

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.

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.

act_so101_rubik_v2 is an open-source robotics policy based on imitation learning and action chunking with transformers. It enables robots to learn and perform complex tasks by predicting short action sequences from teleoperated data. The model is suitable for robotics researchers and developers seeking advanced control policies.

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.

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.

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_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-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.

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_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.

So101 Lego 2cam Narrow V1 49 is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning technique for robotics. Instead of predicting individual actions, ACT predicts short sequences of actions, referred to as action chunks. The model is trained using teleoperated data, which involves learning from demonstrations performed by a human operator. According to the evidence, this approach can often result in high success rates for robotic tasks. The model can be used to run policies on robots or to train new policies, and it is compatible with the LeRobot library. Instructions are provided for using the model with various tools, including LeRobot, Google Colab, and Kaggle. The model and its associated files are distributed under the Apache 2.0 license. So101 Lego 2cam Narrow V1 49 is positioned as a resource for those interested in robotics and imitation learning, particularly for applications that benefit from chunked action prediction. The evidence does not specify further details about its intended audience, supported hardware, or specific robotic platforms.

so101-button-touching-policy-ideal-30 is an open-source imitation learning policy for robotics, trained to predict action chunks from teleoperated data. It is designed for robotics researchers and developers working on autonomous agents and control policies.

so101-brachiomimus-block-movement is an open-source imitation-learning policy model for robotic block movement tasks. It enables robotics researchers and developers to train and deploy policies for manipulation using the LeRobot framework, with open weights and integration guides.

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.

so101-brachiomimus-block-movement-v2 is an open-source AI model for robotics, focused on imitation learning for block movement tasks. It integrates with LeRobot and supports both training and inference, making it suitable for robotics researchers and developers.

so101_smolvla_model is an open-source robotics policy model designed for imitation learning and manipulation tasks, compatible with the LeRobot framework. It supports fine-tuning, Colab integration, and is aimed at robotics researchers and developers seeking customizable policy models.

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.