부산대학교 도서관

Human-centered interactive robot tasks (e.g., social greetings and cooperative dressing) are a type of task where humans are involved in task dynamics and performance evaluation. Such tasks require spatial and temporal coordination between agents in real-time, tackling physical limitations from constrained robot bodies, and connecting human user experience with concrete learning objectives to inform algorithm design. To solve these challenges, imitation learning has become a popular approach where by a robot learns to perform a task by imitating how human experts do it (i.e., expert policies). However, previous works tend to isolate the algorithm design from the design of the whole learning pipeline, neglecting its connection with other modules inside the process (like data collection and user-centered subjective evaluation) from the view as a system. Going beyond traditional imitation learning, this work reexamines robot imitation learning in human-centered interactive tasks from the perspective of the whole learning pipeline, ranging from data collection to subjective evaluation. We present a process-oriented framework that consists of a guideline to collect diverse yet representative demonstrations and an interpreter to explain subjective user-centered performance with objective robot-related parameters. We illustrate the steps covered by the framework in a fist-bump greeting task as demonstrative deployment. Results show that our framework is able to identify representative human-centered features to instruct demonstration collection and validate influential robot-centered factors to interpret the gap in subjective performance between the expert policy and the imitator policy.