so101_pick_cube_v2_act is an open-source robotics model implementing action chunking with transformers for imitation learning. Below are 22 other ai apps with similar functionality to So101 Pick Cube V2 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.
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_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-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_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.
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_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.
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_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.
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.
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.
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.
smolvla_so101_cube_put_take_20260710 is an open-source robotics policy model designed for cube put-and-take tasks, compatible with the LeRobot framework. It allows robotics researchers to run, fine-tune, and evaluate manipulation policies in simulation or on real robots.
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.
smolvla_so101_cube_put_20260710_frozen is an open-source robotics policy model designed for cube manipulation tasks. It is compatible with the LeRobot library and can be used for research, training, and evaluation in robotics environments. The model is distributed via Hugging Face for local use and further experimentation.
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_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.
smolvla_so101_cube_put_20260710 is an open-source robotics policy model for cube placement tasks, designed for use with the LeRobot framework. It allows researchers and engineers to train, fine-tune, and deploy robotics policies using open weights.
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.
