Planning Multimodal Exploratory Actions for Online Robot Attribute Learning

Robots frequently need to perceive object attributes; such as "red;" "heavy;" and "empty;" using multimodal exploratory actions; such as "look;" "lift;" and "shake." Robot attribute learning algorithms aim to learn an observation model for each perceivable attribute given an exploratory action. Once the attribute models are learned; they can be used to identify attributes of new objects; answering questions; such as "Is this object red and empty?" Attribute learning and identification are being treated as two separate problems in the literature. In this paper; we first define a new problem called online robot attribute learning (On-RAL); where the robot works on attribute learning and attribute identification simultaneously. Then we develop an algorithm called information-theoretic reward shaping (ITRS) that actively addresses the trade-off between exploration and exploitation in On-RAL problems. ITRS was compared with competitive robot attribute learning baselines; and experimental results demonstrate ITRS' superiority in learning efficiency and identification accuracy.