ManycoreTech团队的SPEAR论文被ECCV2026接收，将Unreal Engine从视觉模拟器转为可编程机器人训练系统。SPEAR暴露14K个UE函数给Python，以73fps渲染1080p帧，支持脚本化世界、智能体、相机、材质、标签和确定性场景执行。平台基于OpenUSD，形成从真实空间到机器人训练的闭环：数字化→模拟→训练。与Syn-GRPO和WalkerBench共同构成Physical AI的数据、仿真与评估基础设施。
Robotics is hitting a wall that larger models alone cannot climb.
Physical AI cannot scale on videos alone, and these papers show why.
@ManycoreTech research team’s #ECCV2026 accepted paper SPEAR turns Unreal Engine from a visual simulator into a programmable robotics training system.
Its big step is exposing 14K UE functions to Python while rendering 1080p frames at 73fps. Gives you scriptable worlds, agents, cameras, materials, labels, and deterministic scene execution.
#Simulation #EmbodiedAI #SpatialIntelligence
Our papers just got accepted at #ECCV2026 — and the one we're most excited about: SPEAR, our next-gen Physical AI simulation platform, built with multiple tech giants. SPEAR closes the loop from real-world space to robot training: digitize → simulate → train. Alongside Syn-GRPO and WalkerBench, this is our full-stack bet on the data, simulation, and evaluation infrastructure that Physical AI runs on. Built on OpenUSD. Designed for the age of Physical AI. Huge thanks to our SPEAR co-authors and partners: @ros_german, @StefanLeuteneg1, Kalyan Sunkavalli, Vladlen Koltun, Rushikesh Zawar, Rachith Dey-Prakash, and Quentin Leboutet. #PhysicalAI #EmbodiedAI #Robotics #Simulation #ECCV2026 #SpatialAI #OpenUSD在 X 查看被引用的帖子
来源：Rohan Paul · x.com