smolvla_so101_cube_put_20260710 is an open-source robotics policy model for cube placement tasks, designed for use with the LeRobot framework. Below are 18 other ai apps with similar functionality to Smolvla So101 Cube Put 20260710, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
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 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-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_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 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_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_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 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 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.
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_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_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.
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.
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_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.
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.
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.
Act So101 Pick Cube is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning approach for robotics. The ACT method predicts short sequences of actions, referred to as action chunks, rather than individual steps. This technique is designed to learn from teleoperated data, enabling the model to achieve high success rates in robotic tasks. The policy represented by Act So101 Pick Cube has been trained and published using the LeRobot platform, and users are provided with instructions for utilizing the model through various libraries, inference providers, notebooks, and local applications. The documentation points to resources for running the policy on a robot or training a custom policy, with support for platforms such as Google Colab and Kaggle. The model is licensed under the Apache 2.0 license. The evidence also notes that the model is associated with a specific robot type, labeled as so_f. Further technical details, such as the training dataset and configuration, are referenced in the model documentation and linked arXiv paper. No information is provided about pricing, user roles, or specific deployment requirements beyond the platforms and libraries named.
