Open-Source VLA & VLM Robot Models
A curated catalog of open-source Vision-Language-Action (VLA) and Vision-Language (VLM) models for robot manipulation — with links to official sites, GitHub, and Hugging Face.
Foundation VLA Models
Large-scale action models trained with multi-robot, multi-task data.
CollectionModel Comparison Track
Models commonly used for benchmark-style side-by-side evaluation.
CollectionPolicy Learning Models
Architectures optimized for practical IL/RL policy training loops.
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VLA & VLM Models for Robotics
OpenVLA
7B-parameter VLA. Llama 2 + DINOv2/SigLIP. 970K demos from Open X-Embodiment. Outperforms RT-2-X with 7× fewer params. MIT, Hugging Face.
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Octo
Transformer diffusion policy. 27M/93M params. 800K trajectories. Multi-robot, language/goal conditioning. MIT, Hugging Face.
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RT-X / RT-1-X
Open X-Embodiment models. JAX & TensorFlow checkpoints. Multi-robot, language-conditioned. Apache 2.0.
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Spatially guided VLA. Two-stage: grounding + action. 71–81% on Google Robot, 95.9% LIBERO. MIT, Hugging Face.
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RoboFlamingo
OpenFlamingo-based VLM for robot control. Policy head + imitation learning. Strong on CALVIN. MIT, Hugging Face.
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BridgeVLA
3D VLA with input-output alignment. 88.2% RLBench, 64% COLOSSEUM. Heatmap pre-training + point cloud fine-tuning.
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Diffusion Policy
Visuomotor policy as denoising diffusion. +46.9% over prior methods. Receding horizon, time-series transformer. Open source.
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LeRobot
Framework + ACT, SmolVLA (450M). End-to-end IL/RL. Datasets, training, deployment. PyTorch, Hugging Face Hub.
View model →Datasets & Tools to Pair
Practical Model Selection
Compare architectures by task fit, data need, and deployment complexity.
Data-Model Alignment
Model choices are connected to compatible dataset and format stacks.
Experiment Velocity
Open-source links and implementation-ready pointers reduce setup friction.
Scale to Production
From evaluation to deployment with support for tuning and integration.