Lambda 将在 CoRL 期间办分论坛：GPU 加速仿真如何支撑机器人策略训练
机器人方向的朋友可以留意：11 月 12 日在 Austin，Lambda 会用 BEHAVIOR、RoboEval、RoboChess 三个基准拆解仿真到真机的完整流程，想搞懂 sim-to-real 怎么落地的话现场听一手经验比读论文快。
Lambda 宣布 11 月 12 日上午 8:30（美国中部时间）在 Austin 的 JW Marriott 212 会议室举办一场关于具身 AI 的分论坛，主题是 GPU 加速仿真在真实机器人训练中的作用。
核心思路是把仿真当作桥梁：先在仿真环境中跑具身 AI 基准测试，再通过 sim-to-real 迁移把策略部署到真实机器人上学习。活动会用三个具体基准来拆解这条流水线，分别是 BEHAVIOR、RoboEval 和 RoboChess，覆盖家庭任务、机器人评估和对弈三类场景。
GPU-accelerated simulation is the bridge: embodied AI benchmarks in sim, then sim-to-real transfer for real robot policy learning.
Three challenges, BEHAVIOR, RoboEval, RoboChess, make that pipeline concrete.
Nov 12, 8:30 AM CST, Room 212, JW Marriott, Austin.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include sim-to-real, 具身智能, BEHAVIOR, RoboEval.
Source attribution: curated from Lambda (@LambdaAPI).
