so101_smolvla_model is an open-source robotics policy model designed for imitation learning and manipulation tasks, compatible with the LeRobot framework. Below are 26 other ai apps with similar functionality to So101 Smolvla Model, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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_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.
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_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_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-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_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.
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_cube_put_20260710_frozen_200k is an open-source robotics policy model designed for cube manipulation and pick-and-place tasks. It integrates with LeRobot and is suitable for researchers and developers working on robotic control and imitation learning.
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.
smolvla_so100_stack_cube_merged is an open-source AI model designed for robotic control and manipulation tasks. It supports vision-language-action integration and can be used by robotics researchers and developers for automation and 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.
smolvla_so101_pick_place_v5_20k is an open-source vision-language-action model designed for robotic pick-and-place tasks. It integrates with LeRobot and supports policy training and evaluation for robotics researchers.
ur10e_smolvla_2 is an open-source robotics policy model for imitation learning, designed for use with the LeRobot library. It enables developers to train, fine-tune, and deploy policies for robotic arms, supporting research and practical applications in robotics. The model is distributed under an open license on Hugging Face.
Smolvla So100 Stack Cube is a model hosted on Hugging Face, categorized under robotics. The evidence indicates that it is associated with LeRobot, a robotics library, and can be used for tasks related to robotics policies. The model is distributed in the safetensors format and is available under the Apache-2.0 license. Users are provided with instructions for integrating the model with LeRobot, including commands for fine-tuning the model on custom datasets and running the policy using the record function. The evidence references an arXiv paper (arxiv: 2506.01844), suggesting a research context, but does not provide further details about the model's architecture or specific use cases. Access is provided via Hugging Face, and the model can be used with various libraries, inference providers, notebooks, and local applications as indicated by the general instructions. No information is provided about pricing, target user roles, or specific capabilities beyond its connection to robotics and LeRobot.
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.
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.
smolvla-so101-write-5-kadokawa-subtask is an open-source model checkpoint designed for robotics and AI research. It enables researchers to fine-tune, deploy, and experiment with advanced AI models for text and robotics-related tasks. Distributed via Hugging Face, it supports API and local deployment.
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.
smolvla_dohyun_policy is an open-source robotics policy model designed for use with the LeRobot framework. It provides pretrained weights and supports fine-tuning for various robotic control tasks. Ideal for robotics researchers and developers working on machine learning for robotics.
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.
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.
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.
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_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 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.
