Open-source alternatives to Spatial Forcing

This repo provides training and inference code for the paper "Large Video Planner Enables Generalizable Robot Control"

Official implementation of ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver.

Implementation of VLM4VLA

NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards

Dexbotic: Open-Source Vision-Language-Action Toolbox

vjepa2 is a joint-embedding predictive architecture and video self-supervised learning framework. It functions as a visual representation learner and a robotic manipulation model designed to learn representations by predicting future latent states without reconstructing pixels. The system enables the pretraining of video encoders that learn temporally consistent features through masked-token prediction and multi-modal tokenization. It further maps these latent embeddings to specific physical movements via action-conditioned post-training to plan and execute robot arm grasping and picking task

F1: A Vision Language Action Model Bridging Understanding and Generation to Actions

InternVLA-A1: Unifying Understanding, Generation, and Action for Robotic Manipulation​

InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy

NeurIPS 2025 CogVLA: Cognition-Aligned Vision-Language-Action Models via Instruction-Driven Routing & Sparsification

Implementation of RA-L (2026) paper: VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback

WIP Code for LangToMo

Vision-Language-Action Optimization with Trajectory Ensemble Voting

This is the official repository for villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models.

ICRA 2026 VITRA: Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos

Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding.

VLA-0: Building State-of-the-Art VLAs with Zero Modification

giga-brain-0 is a robot action model framework designed to train and deploy neural networks that map multi-modal sensor data to physical robot control signals. It functions as a robot manipulation controller that processes high-dimensional observations to execute dexterous, long-horizon physical tasks. The project provides a multi-modal robot inference server using a client-server architecture to stream real-time vision and language observations for instant action prediction. It includes an embodiment fine-tuning pipeline to adapt pre-trained base models to specific robot hardware configurati

placeholder for original G0 webpage

LLaVA-VLA: A Simple Yet Powerful Vision-Language-Action Model ICRA 2026