物理的整合性を考慮したタスク構造再構成による推論時ロボット行動制御
Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure
学習済みロボットポリシーを再学習せずに、テスト時にユーザーの好みへ行動を誘導する手法ReStructを提案。ニューラルオートマトンでタスク構造を再構成し、物理的に実現可能な制御を実現する。
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著者: Yiyuan Pan, Hanjiang Hu, Shangtao Li, Xusheng Luo, Changliu Liu
分類: cs.RO
原文アブストラクト
A central challenge in deploying learned robot policies is inference-time behavior steering: redirecting a policy at test time to satisfy user preferences not anticipated during training, without retraining. Existing methods fail in two modes: end-to-end methods require fine-tuning or expert-level guidance, while neuro-symbolic methods rely on predefined symbols whose edits can result in logically reasonable but physically infeasible plans. To address this challenge, we propose ReStruct, which builds upon a neural automaton policy that decomposes a visuomotor policy into a high-level state-machine skeleton capturing task structure and a low-level continuous controller represented as a residual policy. Specifically, ReStruct adopts the automaton to represent the preference and incorporates it into the skeleton through a synchronous product, thereby reconfiguring the task structure. With the controller kept frozen, the action priors provided by the skeleton are updated accordingly to enable physically-aware control under a modified task structure. Extensive experiments from simulation and real-world show that ReStruct steers a wide range of preferences, from object-centric specifications to temporal-logic constraints, and after steering surpasses existing methods, exceeding VLA models in both task success and preference-following by up to 25%.
