act_slow is an open-source robotics model that implements action chunking with transformers for imitation learning. Below are 7 other ai apps with similar functionality to Act Slow, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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.
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.
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.
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 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 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.
