Smolvla So100 Stack Cube Alternatives

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 Wrist Top Cube is a model available on Hugging Face that is associated with robotics applications. The evidence indicates that it is intended for use with the LeRobot library, as instructions are provided for integrating the model into LeRobot workflows. Users are guided to install LeRobot and utilize specific scripts for tasks such as fine-tuning the model on a dataset and running the policy with a robot. The model appears to be distributed in the Safetensors format and is referenced with an arXiv preprint, suggesting a research context. The model is released under the Apache-2.0 license, making it open source. There is no information in the evidence about pricing, so it is not possible to determine whether there are paid plans or restrictions beyond the open-source license. The documentation provides command-line examples for training and running the model, indicating that it is aimed at users familiar with Python and robotics software development. The mention of datasets and batch sizes in the training commands suggests that the model is designed to be fine-tuned or adapted to specific robotic tasks, though the precise nature of these tasks is not detailed in the evidence provided. No specific information is given about the types of robots supported, the exact manipulation tasks addressed, or the intended audience beyond those using LeRobot and Hugging Face infrastructure. The evidence does not mention any integrations beyond LeRobot, nor does it describe the model's performance, supported platforms, or hardware requirements. Overall, Smolvla Wrist Top Cube is positioned as an open-source robotics model for use with LeRobot, with capabilities for training and deployment in compatible environments.

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 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.

SO101_DiceStacking_SmolVLA_TopOnly_v1 is an open-source AI model designed for robotics applications, specifically dice stacking tasks. It provides a policy for manipulation and can be integrated into robotics research workflows. Distributed under the Apache 2.0 license, it is suitable for local inference and experimentation.

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_mobile_robot_lift_pick_up_cube_single is an open-source robotics policy model for mobile robots, enabling pick-and-place tasks. Distributed as an adapter compatible with LeRobot, it supports local fine-tuning and integration into robotics workflows.

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.