過去，具身智能因模型龐大、門檻極高，一直由巨頭和實驗室把持。7月19日，面壁智能開源首個具身AI模型系列MiniCPM-Robot，完整放出1.5B參數的視覺-語言-動作模型，讓個人開發者在真實機器人上實現操作與目標跟蹤，開放力度在具身領域罕見。該系列包含三個核心組件。
阿里巴巴發佈千問具身智能大模型Qwen-Robot系列，包括操作模型、移動模型和世界模型三大核心，實現機器人操控、導航與物理規律推理協同。其中Qwen-RobotManip通過80維設計，解決了傳統VLA模型在硬件和場景遷移時能力不足的痛點，標誌着大廠在具身智能基礎模型領域的深化佈局。
2026年5月，自變量機器人開源VLA模型Wall-OSS-0.5，實現零樣本部署突破，打破傳統“考前微調”範式，推動具身智能從定製腳本向通用大腦轉變。
小米近日開源了視覺-語言-動作大模型Xiaomi-Robotics-0的真機後訓練全流程，推動具身智能發展。團隊僅用約20小時任務數據訓練，就讓機器人掌握精準收納耳機等高難度操作，展示了快速學習複雜技能的能力。
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4
VLA-Adapter
VLA-Adapter是一種在Libero-Spatial上訓練的微型視覺語言動作模型，採用Prismatic-VLM架構，僅使用Qwen2.5-0.5B作為大語言模型主幹。該模型在機器人基準測試中超越了參數規模更大的開源VLA模型，實現了高性能的視覺-語言-動作理解與執行。
Hume-vla
Hume-System2是一個雙系統視覺-語言-行動（VLA）模型的系統2預訓練權重，用於加速系統2的訓練，為機器人領域的相關研究和應用提供支持。
UCSC-VLAA
VLAA-Thinker是一個創新的視覺語言模型，能夠同時處理圖像和文本輸入，並生成高質量的文本輸出。該模型基於論文《SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models》的研究成果開發，專注於類似R1的推理能力。
VLAA-Thinker-Qwen2.5-3B是一個類似R1的推理大視覺語言模型，專注於多模態推理任務。該模型在OpenCompass多模態推理排行榜上達到了SOTA性能，支持圖像理解和複雜推理能力。
CogACT
CogACT是一種基於視覺語言模型(VLM)衍生的新型高級視覺語言動作(VLA)架構，專為機器人操作設計。
CogACT是一種新型視覺語言動作(VLA)架構，結合視覺語言模型與專用動作模塊，用於機器人操作任務。
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