LeRobot is a large, modular robotics and robot-learning framework maintained by Hugging Face. Although it contains substantial research code (20+ policy implementations, reward models, dataset formats, and RL infrastructure), its dominant character is that of a production-grade application/library: it exposes a rich CLI surface (lerobot-train, lerobot-eval, lerobot-record, lerobot-calibrate, lerobot-teleoperate, lerobot-annotate, lerobot-rollout), supports real hardware robots, teleoperators, cameras, and motor buses, and provides a well-structured Python package that can be embedded into external applications. The repo is best described as hybrid: a research codebase packaged as an installable robotics application framework.
Alpamayo 1 is a Vision-Language-Action (VLA) model designed for autonomous driving applications. It implements the Alpamayo-R1 architecture that combines a Qwen3-VL backbone for vision-language reasoning with a diffusion-based action expert for trajectory prediction. The system takes multi-camera video and egomotion history as inputs, generates Chain-of-Causation (CoC) reasoning traces, and outputs future trajectories (6.4s horizon at 10Hz). Key components include a unicycle kinematic model for action space conversion, CUDA graph acceleration for efficient repeated inference, and robust geometric transformations. While the repository is noted to receive only limited maintenance as newer versions exist, the current implementation demonstrates strong engineering practices with several reusable components.
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.
This repository contains the research code for UniDriveVLA, a unified Vision-Language-Action (VLA) model for autonomous driving. It couples a Qwen3-VL based understanding expert with sparse 3D perception and trajectory planning experts via a Mixture-of-Transformers design. The codebase supports two parallel experiment tracks: nuScenes (real-world open-loop evaluation) and Bench2Drive (CARLA closed-loop simulation). It includes full training pipelines (staged pretraining on driving QA datasets, then fine-tuning on nuScenes/Bench2Drive), evaluation harnesses for detection, motion, planning, and VQA, and a CARLA leaderboard integration for scenario-based testing. The repository is primarily a research artifact—it is not a production application but rather a comprehensive release of model definitions, training scripts, data converters, and evaluation metrics for reproducing the paper's results. Significant code duplication exists between the nuScenes and Bench2Drive directories, and many paths are hardcoded, reducing portability. The code demonstrates strong engineering for research (custom CUDA ops, distributed training with DeepSpeed, modular mmdet3d plugins) but lacks the polish of a reusable library.