Anticipation guided proactive intention prediction for assistive robots

Abstract

When a person is performing a task, a human observer usually makes guesses about the person's intent by considering his/her own past experiences. Humans often do this when they are assisting another in completing a task. Making guesses not only involves solid evidence (observations), but also draws on anticipated evidence (intuition) to predict possible future intent. Benefits of guessing include, quick decision making, lower reliance on observations, intuitiveness, and naturalness. These benefits have inspired a proactive guess method that allows a robot to infer human intentions. These inferences are intended to be used by a robot to make predictions about the best way to assist humans. The proactive guess involves intention predictions which are guided by future-object anticipations. To collect anticipation knowledge for supporting a robot's intuition, a reinforcement learning algorithm is adopted to summarize general object usage relationships from human demonstrations. To simulate overall intention knowledge in practical human-centered situations to support observations, we adopt a multi-class support vector machine (SVM) model which integrates both solid and anticipated evidence. With experiments from five practical daily scenarios, the proactive guess method is able to reliably make proactive intention predictions with a high accuracy rate.