JustRUA：GPT-6ロボット操作エージェントの制御された比較研究
JustRUA: A Controlled Study of GPT-6 Robot-Use Agents
GPT-6によるロボットアーム操作エージェントの設計要素（ハーネス・行動インターフェース・経験再利用・シーン幾何）を同一枠組みで分解検証し、最小構成のハーネスだけでRoboDojoスコアを29.7から50.3へ向上させた。
著者: Zefan Wang, Yufan Dang, Xuanwei Shi, Mingqi Gao, Cheng Yin, SikYuen Tam, Hanyu Liu, Yasheng Wang, Yaxi Lu, Songyang Zhang, Xin Cong, Yuan Yao
分類: cs.RO, cs.AI
原文アブストラクト
GPT-6 Astra can drive a robot arm directly through a fixed harness, and scores of such robot-use agents on RoboDojo have risen quickly since. Reported systems differ in harness and test subset and often change several designs at once, so it is hard to tell which choices produce the gains. We take these designs apart and test them in one framework, along the harness around the model, the action interface, the reuse of experience and the scene geometry given to the model. A lean harness that keeps the full episode history and stops each motion early goes a long way. Reusing experience changes how the model acts but is not a free lunch, and scene geometry helps most, even when depth is predicted from RGB alone. JustRUA starts from the official L3 harness and keeps only what matters most. Even with no learned policy, self-improvement loop or task-specific tuning, this simple harness raises the score on the RoboDojo simulation benchmark substantially, from 29.7 to 50.3.