This is a robot operation model fine-tuned by Tacoin on the LIBERO libero long benchmark based on the NVIDIA GR00T model. The model uses dual RGB streams and 8-degree-of-freedom state input, and can predict 16-step joint space actions, specifically designed for long-horizon robot operation tasks.

MultimodalSafetensors

π₀.₅ is a vision-language-action model developed by Physical Intelligence, with the ability of open-world generalization, capable of performing robot tasks in brand-new environments and scenarios never seen during training.

MultimodalSafetensors

VLA-Adapter is a micro vision-language-action model trained on Libero-Spatial. It adopts the Prismatic-VLM architecture and only uses Qwen2.5-0.5B as the large language model backbone. This model outperforms open-source VLA models with larger parameter scales in robot benchmarks, achieving high-performance vision-language-action understanding and execution.

MultimodalSafetensorsEnglish

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 achieve competitive performance while reducing computational costs and can be deployed on consumer-grade hardware. This model is trained by LeRobot and is designed specifically for robotics tasks.

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

OmniNeural is the world's first full multimodal model specifically designed for neural processing units (NPUs). It can natively understand text, images, and audio, and can run on various devices such as PCs, mobile devices, cars, the Internet of Things, and robots.

MolmoAct is an open-source robot operation action reasoning model developed by the Allen Institute for Artificial Intelligence. It is built on the Qwen2.5-7B and SigLip2 visual backbone networks and is specifically optimized for single-arm Franka robot operation tasks in home and desktop environments.

MultimodalTransformersEnglish

MolmoAct is a fully open-source robot operation action reasoning model developed by the Allen Institute for Artificial Intelligence. It is built based on the Qwen2.5-7B and SigLip2 visual backbone networks and is specifically designed for robot operation tasks in home and desktop environments.

MultimodalTransformersEnglish

MolmoAct is an open-source action reasoning model developed by the Allen Institute for Artificial Intelligence, specifically designed for robot operation tasks. This model is built on Qwen2.5-7B and SigLip2, and is trained on 93 operation tasks in home and desktop environments, with leading vision-language-action processing capabilities.

MultimodalTransformersEnglish

MolmoAct is an open - source action reasoning model developed by the Allen Institute for Artificial Intelligence, specifically designed for robot operation tasks. This model is based on the Qwen2.5 - 7B language model and the SigLip2 visual backbone network, and is trained on 93 unique operation tasks in home and desktop environments, with leading visual - language - action reasoning performance.

MultimodalTransformersEnglish

A visual motion control model based on the diffusion strategy, which treats robot control as a generative diffusion process and can generate smooth multi-step action trajectories. It performs excellently in rich contact operation tasks.

Computer VisionSafetensors

Cosmos-Predict2.5 is a high-performance pre-trained world foundation model suite developed by NVIDIA specifically for physical AI. Based on diffusion model technology, it can generate high-quality images and videos with physical awareness based on text, image, or video input, providing world simulation capabilities for applications such as autonomous driving and robotics.

MultimodalDiffusers

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

A robot strategy model trained based on LeRobot for the task of picking up the power gem.

MultimodalSafetensors

Hume-Libero_Goal is a Vision-Language-Action model based on dual-system thinking, designed specifically for robot tasks, integrating System-2 thinking to improve decision-making ability.

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

A robot strategy model trained based on the LeRobot framework for specific task execution

MultimodalSafetensors