RoboIRS: 推論時の内部表現操作による汎用ロボットポリシーの性能改善
RoboIRS: Inference-Time Internal Representation Steering for Generalist Robot Policies
成功・失敗ロールアウトから線形分類器を学習し、ポリシーの内部表現を推論時に操作することで、パラメータ更新なしに分布外タスクでの成功率を向上させる手法を提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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4. どうやって有効だと検証した？
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6. 次に読むべき論文は？
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著者: Jiuzhou Lei, Chang Liu, Dayou Li, Zhiyuan Zhang, Xiao Liang, Yu She, Zhiwen Fan, Minghui Zheng
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) and world-action models (WAMs) often degrade under out-of-distribution task variations despite retaining partial task capability. To recover such capability, we propose RoboIRS, an inference-time internal representation steering method that uses successful and failed rollouts to train linear classifiers, select outcome-relevant intervention locations, and derive task-specific steering directions without updating policy parameters. On 15 simulation tasks with a frozen $π0.5$ policy, RoboIRS improves the average success rate from 44.4% to 66.2%, outperforming alternative inference-time intervention baselines while adding little inference time. We further validate RoboIRS on real-robot manipulation using the same $π0.5$ policy and demonstrate its applicability to a world-action model Cosmos Policy, where the average success rate improves from 35.4% to 55.4%. These results show that directly steering internal robot-policy representations can improve the performance of robot policies at inference time. Project website is available at https://rollingoat.github.io/roboirs/.