smolvla_ball_cup is an open-source robotics policy model for ball-cup sorting tasks, designed for use with the LeRobot framework. Below are 9 other ai apps with similar functionality to Smolvla Ball Cup, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
ball_cup_smolvla_try3_model is an open-source robotics policy model for ball-and-cup manipulation tasks. It is intended for robotics researchers and developers to train, evaluate, and deploy manipulation policies using LeRobot and Python.
ball_cup_act_hil_round1_model is an open-source imitation learning model for robotics, designed for action chunking tasks. It is distributed on Hugging Face and can be used via API or CLI, supporting research and development in robotic control and 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_pick_the_ball is an open-source robotics policy model designed for pick-and-place tasks. It integrates with the LeRobot framework and can be installed via pip for use in robotics research and automation projects. The model is distributed under the Apache-2.0 license and is suitable for developers and researchers in robotics.
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 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_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_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-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.
