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. Below are 17 other ai apps with similar functionality to Smolvla So101 T1, 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_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_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_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_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 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-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_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.
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.
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.
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 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_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 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.
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.
smolvla_ball_cup is an open-source robotics policy model for ball-cup sorting tasks, designed for use with the LeRobot framework. It enables robotics researchers and developers to train, fine-tune, and deploy sorting policies in simulation or on real robots.
molmoact2-so101-wall is an open-source robotics foundation model that maps camera images and language instructions to robot actions. It supports fine-tuning and integration with robotics libraries, making it suitable for robotics researchers and developers.
