So101 Smolvla Cube In Case is a robotics model available on Hugging Face, associated with the LeRobot framework. Below are 23 other ai apps with similar functionality to So101 Smolvla Cube In Case, 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_20260716_frozen is an open-source robotics policy model designed for cube manipulation tasks. It enables robotics researchers and developers to run and fine-tune the policy locally for research and 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_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_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 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_pick_cube_v2_act is an open-source robotics model implementing action chunking with transformers for imitation learning. It predicts short action sequences for robotic manipulation, enabling efficient policy inference and training. Designed for robotics researchers and developers, it integrates with LeRobot and supports evaluation and further training.
so101_smolvla_model is an open-source robotics policy model designed for imitation learning and manipulation tasks, compatible with the LeRobot framework. It supports fine-tuning, Colab integration, and is aimed at robotics researchers and developers seeking customizable policy models.
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.
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.
act_so101_cube_left_right_v2 is an open-source imitation-learning model for robotics, designed to predict and execute action chunks based on teleoperated data. It supports integration with robotics frameworks and is suitable for researchers and developers working on robotic control and policy learning.
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.
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.
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.
act_so101_pick_cube_v2 is an open-source imitation learning model designed for robotics applications. It predicts short action chunks for robots, enabling them to perform complex tasks by learning from teleoperated demonstrations. The model is available on Hugging Face under an Apache-2.0 license and can be integrated with robotics libraries such as LeRobot. It is intended for robotics researchers and developers seeking advanced policy models for robot control.
act_policy_so101_cube_multitask_0710 is an open-source robotics policy model for multitask cube manipulation, leveraging imitation learning and transformer architectures. It integrates with LeRobot and is intended for robotics researchers and developers.
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.
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_so101_pick_place_v1 is an open-source robotics policy model for pick-and-place tasks, designed for use with the LeRobot library. It enables robotics developers to implement and fine-tune automation workflows in simulation or on physical robots. The model is distributed via Hugging Face and can be installed using pip.
