SmolVLA is a compact and efficient vision-language-action model that can achieve competitive performance while reducing computational costs and can be deployed on consumer-grade hardware. This model is trained using LeRobot and is specifically designed for robotics applications.

MultimodalSafetensors

SmolVLA is a compact and efficient vision-language-action model that can maintain competitive performance while reducing computational costs and is suitable for deployment on consumer-grade hardware. This model is trained using the LeRobot framework and is specifically optimized for robotics tasks.

MultimodalSafetensors

SmolVLA is a compact and efficient vision-language-action model that can achieve competitive performance while reducing computational costs and can be deployed on consumer-grade hardware. This model is trained using the LeRobot framework and is specifically designed for robotics applications.

MultimodalSafetensors

RoboBrain2.0-7B is a powerful model designed specifically for the robotics field, with significant improvements in multi-agent task planning, spatial reasoning, and closed-loop execution.

MultimodalTransformers

Hume-Libero_Object is a dual-system vision-language-action model trained on the Libero-Object dataset. It has System 2 thinking ability and is suitable for research and applications in the field of robotics.

MultimodalTransformersEnglish

Hume-System2 is the pre-trained weights of System 2 for a dual-system Vision-Language-Action (VLA) model, used to accelerate the training of System 2 and provide support for relevant research and applications in the field of robotics.

MultimodalTransformersEnglish

Cosmos-Reason1 is a physical artificial intelligence model developed by NVIDIA. It has the ability to understand physical common sense and can generate embodied decisions through long-chain thinking reasoning. This model supports multimodal input (text + video/image) and outputs text, which is suitable for physical AI fields such as robotics and autonomous driving.

MultimodalTransformersEnglish

A PyTorch-based action recognition model for robotics applications

Computer VisionSafetensors

π0+FAST is an efficient action tokenization scheme designed for robotics by Physical Intelligence, suitable for vision-language-action tasks.

MultimodalSafetensors

A motion control model based on PyTorch, suitable for the field of robotics

Computer VisionSafetensors

MiniVLA is a vision-language-action model focused on robotics, supporting multimodal tasks from image-text to text.

MultimodalTransformersEnglish

MiniVLA is a compact yet high-performance vision-language-action model, compatible with Prismatic VLMs training scripts, suitable for robotics and multimodal tasks.

MultimodalTransformersEnglish

MiniVLA is a 1-billion-parameter vision-language model compatible with the Prismatic Vision-Language Model codebase, suitable for robotics and multimodal tasks.

MultimodalTransformersEnglish

RDT-170M is a 170-million-parameter imitation learning diffusion Transformer model designed for robot vision-language-action tasks.

MultimodalTransformersEnglish

A motion control model based on PyTorch, focusing on motion control tasks in the field of robotics.

MultimodalSafetensors

This model is a vision-language-action model obtained by fine-tuning the OpenVLA 7B model using the LoRA method on the LIBERO-10 dataset, suitable for the field of robotics.

MultimodalTransformersEnglish

This is an OpenVLA 7B vision-language-action model fine-tuned using LoRA technology on the LIBERO-Goal dataset, suitable for the robotics field.

MultimodalTransformersEnglish

This is a vision-language-action model fine-tuned using LoRA on the LIBERO-Object dataset, specifically designed for robotics.

MultimodalTransformersEnglish

An RDT model derived from robotics-diffusion-transformer/rdt-1b, focusing on the field of robotics.

MultimodalTransformersEnglish

A 1-billion-parameter imitation learning diffusion Transformer model pretrained on 1M+ multi-robot operation data, supporting multi-view visual-language-action prediction

MultimodalTransformersEnglish