REKI: a 2.4B vision-language-action model pretrained on public data only, with a memory of its recent past at negligible cost.
仅需Python基础,从0构建自己的具身智能机器人;从0逐步构建VLA/OpenVLA/SmolVLA/Pi0, 深入理解具身智能
StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
An Open-World Foundation Model for General-Purpose Embodied Intelligence.
A curated list of awesome LLM/VLM/VLA/World Model for Autonomous Driving(LLM4AD) resources (continually updated)
Official code of Motus: A Unified Latent Action World Model
A Curated List of Vision-Language-Action (VLA) and World Action Models (WAM) Research and Beyond
An all-in-one VLA engineering platform for embodied AI — from data to real-robot deployment.
[ICLR 2026] The offical Implementation of "Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model"
本项目旨在为致力于进入VLA(Vision-Language-Action)领域的算法工程师提供一份全中文、实战导向的学习/面试手册。 不同于通用的 CV/NLP 面试指南,本项目聚焦于 Robotics 特有的挑战
TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM
Open source evals for physical AI. Run any LLM/VLA on any arm/humanoid against any real/sim benchmark.
[ICLR 2026] ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
[ECCV 2026] VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
🌐 Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
A comprehensive list of excellent research papers, models, datasets, and other resources on Vision-Language-Action (VLA) models in robotics.
A unified framework for training, fine-tuning, and evaluating World Action Models
[NeurIPS 2026] Official code of "StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models"
[NeurIPS 2026] Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models
SimpleNav: A simple, unified, reproducible, and extensible framework for navigation VLA research.
Turn world-model imagination into corrective training data for robot policies — a single-GPU, falsifiable pipeline (SmolVLA · LIBERO · CEM · paired simulator replays).
Companion repository for “In-Context Learning for Robots: Methods and Applications” — methods, datasets, and benchmarks organized by taxonomy and release date.
SmolVLA (SmolVLM2-500M) vision-language-action on real dual AgileX Piper arms — continuous authority blending on a proven ACT safety base folds a real towel. One command runs the full 750-step skill (status=0). Full illustrated course included
RadixQuantVLA is a unified, Lego-like framework for post-training quantization, benchmarking, and deployment preparation of Vision-Language-Action models, integrating QuantVLA, Omega-QVLA, OpenVLA, UniVLA, StarVLA, GR00T, and Pi0.5 routes with standardized launchers and validation on LIBERO benchmarks.
A curated, learning-friendly list of tactile sensing research resources for robotic manipulation: papers, datasets, benchmarks, simulation platforms, and frameworks, covering the integration of touch with Vision-Language-Action (VLA) models and World Models for contact-rich manipulation
Native-video memory for vision-language-action models, using timestamped visual history and exact streaming inference for long-horizon robot manipulation.
VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models
Official codebase for Unitas: A 3D-Native World Action Model for Embodied Manipulation
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
2,000 curated embodied AI and robotics resources with bilingual metadata and transparent ranking
HuRo: Robotizing Human Videos for Scalable VLA Pretraining (CoRL 2026)
Grounded Action Model: 3D Grounding as a Foundation for Robotics
Fewer Tokens, Better Action: GPT-6 Astra Robot Agents with 14% Higher Success but 65% Fewer Tokens
RL²-VLA: Adaptive RL Latent Compositional Steering with Test-Time Scaling for Vision-Language-Action Models
🚗 AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding. 🏋️ 512 H800 GPUs · ~2 days · 318K+ video hours · 513B+ training tokens. ⚡ 12 Hz on-vehicle deployment. SFT + GRPO + NAVSIM.
Official implementation of PhaseLoRA: control-regime-conditioned low-rank adaptation for continuous-action vision-language-action policies, built on OpenPI.
FoldQuant: offline folding for W4A4 / W8A8 vision-language-action inference on edge GPUs
Official Implementation for “DeCAL: Towards Physically-Grounded Dexterous Vision-Language-Action Models via Contact-Aware Latent Co-Imagination” (CoRL 2026)