ロボットへの操作タスク教示における人間の音声パターンの理解
Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots
ロボットに多段階の操作タスクを教示する際の人間の音声を、発話時間・韻律・意味内容の3特徴で分析し、教示条件やエラーなど4次元で音声が豊富な情報を伝えることを明らかにした。
著者: Akanksha Saran, Kush Desai, Mai Lee Chang, Rudolf Lioutikov, Andrea Thomaz, Scott Niekum
分類: cs.RO
原文アブストラクト
Humans use audio signals in the form of spoken language or verbal reactions effectively when teaching new skills or tasks to other humans. While demonstrations allow humans to teach robots in a natural way, learning from trajectories alone does not leverage other available modalities including audio from human teachers. To effectively utilize audio cues accompanying human demonstrations, first it is important to understand what kind of information is present and conveyed by such cues. This work characterizes audio from human teachers demonstrating multi-step manipulation tasks to a situated Sawyer robot using three feature types: (1) duration of speech used, (2) expressiveness in speech or prosody, and (3) semantic content of speech. We analyze these features along four dimensions and find that teachers convey similar semantic concepts via spoken words for different conditions of (1) demonstration types, (2) audio usage instructions, (3) subtasks, and (4) errors during demonstrations. However, differentiating properties of speech in terms of duration and expressiveness are present along the four dimensions, highlighting that human audio carries rich information, potentially beneficial for technological advancement of robot learning from demonstration methods.
