本当に重要なのは何か？BEHAVIOR-1Kにおけるπ0.5の推論設定のペア評価
What Actually Matters? A Paired Evaluation of Inference Settings for $π_{0.5}$ on BEHAVIOR-1K
BEHAVIOR-1Kの全100タスクでVLAモデルπ0.5の推論・データ収集戦略を体系的にペア評価し、既存戦略が新しい環境やタスクに一様には転用できないことを示した。
著者: Ruben Hammele, Samira Huber, Mohammad Nazeri, Amirreza Payandeh, Alexandyr T. Card, Aaron Nguyen, Anuj Pokhrel, Daeun Song, Xuesu Xiao, Sören Pirk
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Vision-Language-Action (VLA) models offer a promising paradigm for general-purpose household robotics. However, inference and data-collection strategies that have been deployed in smaller-scale settings do not necessarily generalize to large-scale, diverse task suites. To address this gap, we conduct a systematic, paired evaluation of previously proposed inference and data-collection strategies across all 100 tasks. Our evaluation uses a fine-tuned $π_{0.5}$ model based on the 2025 BEHAVIOR Challenge winner, which achieves a Q-score of 0.39 on the public test set. These results show that proposed strategies do not transfer uniformly to new environments and tasks. Project page, videos, and detailed analysis: https://rubenhammele.github.io/behavior2026