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General-purpose robot policies must work across robots, tasks, and environments, yet the variation that makes physical trials informative also makes them difficult to reproduce. We introduce RoboColosseum, a distributed evaluation framework that combines unified policy serving and standardized protocols across a networ
阅读原文 · Tan Sang Nguyen, Yi Ru Wang, Shailesh Shailesh, Gang Yang, Jiamo Hu, Jianuo Jiang, Haoquan Fang, Zhongzheng Ren, Zhongyi Luo, Ian Chong, Aaron Tung, Santiago Pravisani, Minghui Lin, Yao Mo, Ishneet Singh, Benjamin Tee, Harold Soh, Siddhartha Srinivasa, Dieter Fox, David Hsu, Haoming Song, Maxime Ellerbach, Thomas Wolf, Pepijn Kooijmans, Ruijie He, Michael Cho, Jiafei Duan
向阳乔木 · opencode-go/deepseek-v4.1-flash
机器人策略的评测,一直有个尴尬:实验室里跑分很漂亮,换个机器人、换个房间、换个任务就未必成立。想认真比,就得真机真环境反复跑,成本高、难复现,各家还各用各的协议。 RoboColosseum 想动的是这块地基。它把评测做成分布式:统一策略服务,加标准化协议,接入一批分散在不同环境、由本地团队操作的机器人。这次先跑了 5 个 VLA 策略和 3 个前沿 AI...