FluxVLA Engine V2: 9.64x Speedup and 99.4% Success Rate in Embodied AI
FluxVLA Engine V2: 9.64x Speedup and 99.4% Success Rate in Embodied AI 💡 LimX Dynamics has released the second version of its open-source engineering foundation, FluxVLA Engine. This update significantly expands simulation evaluation and real-robot deployment capabilities, introducing online reinforcement learning (RL) post-training. The report details the integration of 14 distinct strategies into a unified LIBERO closed-loop evaluation framework. Furthermore, it covers 8 strategies each on RoboCasa GR-1, RoboTwin, and RoboDojo benchmarks. On the inference side, optimized model paths achieved frequency boosts ranging from 2.31 to 9.64 times across 8 model-device combinations. The platform has successfully validated the deployment chain from model checkpoints to real-robot closed-loop execution on ALOHA, Rebot, dual-arm Franka, and LimX’s full-size humanoid robot, LimX Oli. ⚡ A key highlight of this update is the implementation of online RL. Using SmolVLA as a case study, paired experiments showed that after online RL post-training, success rates on LIBERO-10 and RoboTwin Clean ten-task suites increased by 10.0 and 15.4 percentage points, respectively. Since its open-sourcing on April 8, FluxVLA Engine has evolved rapidly. The V2 report emphasizes four core engineering principles: All-in-one configuration, modular decoupling, deployment-oriented system optimization, and experiment reproducibility. This approach aims to transform "workshop-style" repetitive engineering into a standardized industrial pipeline, allowing teams to reuse infrastructure and focus on algorithmic innovation rather than environment setup. 🤔 The engine addresses the critical gap between simulation and real-world performance through three technical pillars. First, it standardizes data and model interfaces, supporting architectures like Qwen-VL, GR00T, and π-series. In tests, 11 out of 13 local checkpoint strategies achieved over 95% average success on LIBERO, with DiT4DiT reaching 98.65%. Second, it integrates RLinf for online RL, enabling flow-matching strategies to enter interactive training without re-adaptation. Third, it employs "microscope-level" evaluation, tracking task progress across five dimensions: generalization, memory, precision, long-horizon tasks, and open instructions. This granular feedback helps identify specific failure points, such as visual perception or fine motor control, reducing wasted computational resources. 🚀 For real-world deployment, FluxVLA Engine implements a "universal socket" architecture with decoupled Runner (brain) and Operator (body) interfaces. This allows seamless migration across different robot platforms. In real-robot tests, π0.5 achieved a 72.22% success rate on ALOHA, while GR00T N1.7 reached 88.00% on dual-arm Franka. On the LimX Oli humanoid, π0.5 completed 61.67% of the first three stages of a complex box-carrying task. The system also features a Human-in-the-loop module for data flywheel acceleration. In an ALOHA towel-folding task, using ARM advantage-weighted behavior cloning boosted success rates from a 62.1% baseline to 99.4%, while increasing throughput from 18 to 32 episodes per hour. 📌 Что это значит на практике: FluxVLA Engine V2 превращает разработку embodied AI из хаотичного процесса в стандартизированную конвейерную линию. Инженеры получают готовую инфраструктуру для быстрого тестирования новых моделей и сбора данных, что снижает барьер входа и ускоряет итерации. Это позволяет командам фокусироваться на алгоритмах, а не на рутинной настройке окружения. Перевод и адаптация: AI Lab. Источник: QbitAI (量子位), https://www.qbitai.com/2026/10/506194.html #AI #EmbodiedAI #Robotics #FluxVLA #LLM #Engineering