act_DS3_1B1O_FV_removeidle_rand20 is an open-source model for robotic action chunking using imitation learning. Below are 16 other ai apps with similar functionality to Act DS3 1B1O FV Removeidle Rand20, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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_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.
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.
smolvla_DS3_1B1O_FV_removeidle_rand60 is an open-source AI model focused on robotics and policy learning. It supports integration with robotics frameworks and can be fine-tuned for custom tasks, making it ideal for robotics researchers and developers.
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.
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.
Smolvla DS3 1B1O FV Removeidle Rand40 is a model available on Hugging Face, associated with the robotics domain. The model is referenced in connection with LeRobot, and instructions are provided for using it with this library. The evidence indicates that users can clone the LeRobot repository, install it with a specific option for smolvla, and launch finetuning of the model on a dataset using a provided script. The example given uses a dataset identified as lerobot/svla_so101_pickplace, and allows configuration of batch size, training steps, output directory, job name, device, and integration with wandb for tracking. The model is distributed under the Apache 2.0 license, and is available in the safetensors format. There is also a reference to arxiv: 2506.01844, suggesting a related publication. The evidence does not provide further details about the model's specific capabilities, intended audience beyond robotics and LeRobot users, or additional features. Pricing information is not mentioned, but the Apache 2.0 license indicates it is open source. No claims are made about integrations beyond LeRobot, nor about supported platforms other than those implied by the use of Python scripts and CUDA devices.
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.
This Hugging Face model provides an imitation-learning policy for robotics, predicting action chunks from teleoperated data. It is open source and can be integrated into robotics workflows via API or CLI, supporting research and development in robotics automation.
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.
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.
Task1 is a robotics policy model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation learning approach designed to predict short sequences of actions, or action chunks, rather than individual steps. The model is trained using teleoperated data and is intended to achieve high success rates in robotic control tasks. Task1 has been trained and uploaded to the Hugging Face Hub, where it can be accessed and used with various libraries, including LeRobot, as well as through platforms such as Google Colab and Kaggle. The model is suitable for use with robots of type 'so_follower' and is compatible with side and window-side camera inputs. The model card provides information about the model’s inputs and outputs, training dataset, and configuration, as well as instructions for running the policy on a robot or training a custom policy. Task1 is distributed under the Apache-2.0 license, making it available for open-source use. The evidence does not specify further details about its intended audience or additional features beyond those mentioned.
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.
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.
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_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.
