smolvla_DS1_1B1O_FF_removeidle_rand80 is an open-source robotics policy model distributed via Hugging Face. It is designed for use with the LeRobot framework and supports fine-tuning and deployment in robotics research. Below are 26 other ai apps with similar functionality to Smolvla DS1 1B1O FF Removeidle Rand80, 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 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.
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.
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_DS1_1B1O_FF_removeidle_rand80 is an open-source imitation learning policy model for robotics, trained using the LeRobot framework. It predicts action chunks for robotic control and is intended for researchers and developers working on robotic automation and learning from demonstration.
smolvla_so101_v5_30k is an open-source robotics policy model for controlling SO101 robots, compatible with the LeRobot library and Python. It is designed for robotics developers and researchers to implement imitation learning and automate robotic tasks. The model is distributed via Hugging Face.
smolvla-so101-candy-33c62cfe is an open-source robotics policy model designed for use with the LeRobot framework and similar robotics platforms. It enables researchers and developers to train, evaluate, and deploy robotic control policies using open weights and local inference.
smolvla_so101_t1_v3 is an open-source robotics policy model for imitation learning and control tasks. It integrates with LeRobot and supports training, evaluation, and deployment for robotics research and development.
act_DS1_1B1O_FF_removeidle_rand60 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.
smolvla_p3_redcube_merged is an open-source robotics policy model designed for manipulation and control tasks. It is suitable for robotics researchers and developers seeking to implement or fine-tune policies for robotic arms and similar systems.
Smolvla So101 Candy 63d2cc57 is a model hosted on Hugging Face and associated with the Qualia Robotics organization. The model is referenced in connection with robotics applications, as indicated by its integration instructions for use with the LeRobot library. Users are provided with commands for fine-tuning the model on custom datasets and for running the policy using specific scripts, suggesting its intended use in training and deploying robotics policies. The documentation points to a repository and provides sample commands for cloning and installing the LeRobot library, as well as for launching fine-tuning and running the policy, which implies that the model is designed to be used within Python-based robotics workflows. The model card references the arXiv preprint 2506.01844, indicating that there is a research publication associated with the model, although no further details about its contents or capabilities are provided in the evidence. The license is explicitly stated as Apache-2.0, confirming that the model is available under an open-source license. The evidence also notes that the model is available in the EU region and can be used with various libraries, inference providers, notebooks, and local applications, though specific details about these integrations are not elaborated. No information is provided about the specific features, architecture, or intended user base beyond its association with robotics and compatibility with LeRobot. There are no explicit claims about supported input types, performance, or other technical specifications. Pricing details are not mentioned, but the Apache-2.0 license suggests it is free to use under open-source terms. Overall, Smolvla So101 Candy 63d2cc57 is positioned as an open-source model for robotics-related tasks, with available integration instructions for Python-based robotics libraries.
smolvla_so101_t2_v3 is an open-source robotics policy model for imitation learning, distributed via Hugging Face. It enables researchers and developers to train, evaluate, and deploy manipulation policies for robotic systems, supporting fine-tuning and integration with robotics frameworks.
Smolvla Libero10 Finetune is a robotics policy model available on Hugging Face, designed for use with the LeRobot library. The model is intended for robotics applications and provides instructions for integration with LeRobot, including steps for installation, finetuning on custom datasets, and running the policy using specific scripts. Users can clone the LeRobot repository, install the necessary components, and launch finetuning of the model on their own datasets by following the provided command-line instructions. The workflow supports configuration of parameters such as batch size, number of training steps, output directory, job naming, device selection, and integration with monitoring tools. The model can also be deployed to run policies with the record function, interfacing with specified robot types and ports. 0 license, allowing for open-source use and modification. It is positioned within the robotics domain and is suitable for those seeking to train or deploy policy models using the LeRobot framework.
ur10e_smolvla_1 is an open-source robotics policy model designed for controlling UR10e robots via the LeRobot framework. It supports multimodal inputs and enables researchers to train, fine-tune, and deploy advanced robot control policies. The model is suitable for robotics research and practical applications in automation.
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.
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.
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 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.
smolvla_so101_t2_v2 is an open-source robotics model designed for use with the LeRobot library. It supports fine-tuning on custom datasets and deployment in simulation environments, making it suitable for robotics engineers and researchers.
smolvla_mir1_v2 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_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_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.
smolvla_white_in_blue_circle is an open-source AI model for robotics control and policy learning, distributed with model weights and instructions for use with the LeRobot library. It is intended for robotics researchers and developers building control systems.
smolvla_so100_stack_cube_v2 is an open-source robotics policy model designed for stacking cube tasks. It integrates with LeRobot and can be used via CLI tools, supporting fine-tuning and research in robotic manipulation.
dofbot_smolvla_full32 is a pre-trained robotics policy model designed for use with LeRobot and Dofbot robots. It enables researchers and engineers to control robots for automation and experimentation purposes.
smolvla_piper_v6 is an open-source robotics policy model designed for pick-and-place automation tasks. It integrates with LeRobot and Python, allowing robotics developers and researchers to deploy, train, and evaluate policies for robotic arms and automation workflows.
smolvla_libero_finetune is an open-source, fine-tuned model for robotics applications, compatible with the LeRobot library. It provides pretrained weights and scripts for training and deployment, supporting custom datasets and advanced robotic control tasks.
