FluxVLA is a comprehensive, config-driven framework for training, evaluating, and deploying Vision-Language-Action (VLA) models for robotic manipulation. It supports multiple VLA architectures (OpenVLA, Pi0/Pi0.5, GR00T, Cosmos3, DreamZero, etc.) and robot platforms (LIBERO simulation, RoboCasa, Franka, UR3, ALOHA, Oli humanoid, Tron2). The codebase provides a modular pipeline with registries and builders for datasets, transforms, models (backbones, heads, projectors), optimizers, collators, and inference runners. Production capabilities include distributed training (FSDP/DDP), real-time ROS inference, custom CUDA/Triton kernels for performance, and evaluation with metrics reporting to Feishu. While primarily an engineering framework for applied robot learning, it also contains research contributions such as flow-matching action heads, reward models (ARM, SARM), and real-time chunking (RTC) guidance. The architecture is clean and extensible, though config proliferation and vendored third-party code introduce maintenance overhead.
The repository implements LLaVA-VLA, a Vision-Language-Action model for robotic manipulation tasks. It extends the LLaVA framework to predict action sequences from visual inputs, proprioceptive state, and language instructions using a diffusion-based action head. The system includes training pipelines for fine-tuning on datasets like CALVIN, inference servers (Flask and Gradio) for real-time robot control, and evaluation tools for benchmarks. The architecture features modular vision encoders, projectors, and resamplers with factory patterns for flexibility, action tokenization for discrete action prediction, and integration with PEFT/DeepSpeed for efficient training.