{"paper":{"title":"When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Reformulating BFM task inference as a minimax problem over dynamics perturbations yields robust policies from single-environment offline data alone.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ashutosh Nayyar, Rahul Jain, Rishabh Agrawal","submitted_at":"2026-05-16T14:33:34Z","abstract_excerpt":"Behavior Foundation Models (BFMs) enable scalable imitation learning (IL) by pretraining task-agnostic representations that can be rapidly adapted to new tasks. However, existing BFMs assume fixed environment dynamics, limiting their robustness under real-world shifts such as changes in friction, actuation, or sensor noise. We address this by formulating BFM task-inference as a robust minimax optimization problem, enabling adaptation to worst-case dynamics perturbations without modifying pretraining. To the best of our knowledge, this is the first BFM-based framework that achieves robustness t"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"To the best of our knowledge, this is the first BFM-based framework that achieves robustness to dynamics shifts while relying solely on offline data from a single nominal environment.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The minimax optimization over dynamics perturbations can be solved tractably from offline nominal data alone and produces policies that generalize to actual (not just modeled) dynamics shifts.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Robust minimax task inference in BFMs achieves dynamics-shift robustness from nominal offline data alone and outperforms standard 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