脚式ロボット聴覚のためのオープンセット自己雑音分離：アノテーション不要適応と事前学習モデル転送
Open-Set Ego-Noise Separation for Legged-Robot Audition via Annotation-Free Adaptation and Pretrained-Model Transfer
脚式ロボットの歩行による自己雑音を、事前にクラスを指定しない環境音を保ちながら分離するフレームワークを提案。未ラベル録音から自己雑音クリップを自動選択し、多様な環境音と混合して教師信号を作り、汎用分離器をロボットに適応させる。
著者: Koki Shoda, Jun Younes Louhi Kasahara, Aoba Koyanagi, Qi An, Atsushi Yamashita
分類: cs.RO
原文アブストラクト
This paper proposes an open-set ego-noise separation framework for legged-robot audition via annotation-free adaptation and pretrained-model transfer. The framework removes robot-specific ego-noise while preserving environmental sounds whose classes are not specified in advance. Acoustic sensing provides cues about a robot's surroundings beyond the visual field, but walking-induced ego-noise from footstep impacts, joint-backlash rattling, and motor noise severely contaminates the recordings. The framework first uses RecurGraph to select ego-noise-dominant clips from the unlabeled recordings by aggregating clip embeddings into an embedding centroid and propagating scores over an audio-embedding graph. The selected clips are mixed with diverse environmental sounds from a large-scale sound-event dataset to provide paired mixture--target supervision for open-set separation. Transfer-DiT then adapts a general-purpose zero-shot neural separator to achieve high-fidelity open-set ego-noise separation for the target robot. Experiments with bipedal and quadrupedal robots show reliable clip selection and improvements in separation quality and downstream task performance over baseline separators. These results demonstrate the feasibility of annotation-free adaptation without separately recorded ego-noise-only data or manual clip-level annotations.