FlowEval：実環境展開時に身体性基盤モデルはどれだけ適応が必要か
FlowEval: How Much Adaptation Does an Embodied Foundation Model Need at Deployment?
少数の成功デモンストレーションから、事前学習済み身体性基盤モデルが対象環境にどれだけ適合しているか・追加適応データがどれだけ必要かを、学習不要のフローマッチング指標で診断する手法を提案。
著者: Zhang Xi-Jia, Roman Yakunin, Shivang Chopra, Chengyue Huang, Che Chen, Siddharth Karamcheti, Zsolt Kira
分類: cs.RO
原文アブストラクト
Large-scale pretraining is reducing the amount of adaptation required to deploy embodied foundation models, but it is not guaranteed that a pretrained model already provide the level of performance required in every deployment. Practitioners therefore need to understand how well a pretrained model matches a target deployment and how much adaptation data is left to be collected. We study whether a small set of successful target-domain demonstrations can reveal whether and how much target-domain data will be required before any adaptation. We introduce FlowEval, a set of training-free metrics that use the flow-matching objective to probe an embodied foundation model with a set of demonstrations. FlowEval quantifies predictive disagreement on successful target demonstrations to assess how well a pretrained model fits a target deployment. Across four embodied foundation model families: Pi-0.5, MolmoAct2, GR00T, and DreamZero, FlowEval distinguishes seen from unseen deployments across real-world and simulated manipulation. Further, by finetuning Pi-0.5 and GR00T on 17 RoboCasa365 manipulation tasks, we showed that FlowEval correlates with how success rate improves as additional adaptation data is provided. Together, these results suggest that FlowEval can serve as a lightweight, training-free diagnostic of how much model--deployment adaptation may be required.