Planning Multimodal Exploratory Actions for Online Robot Attribute Learning
This work addresses the challenge of enabling robots to efficiently perceive object attributes in real-time, though it is incremental as it builds on existing attribute learning methods by integrating learning and identification.
The paper tackles the problem of online robot attribute learning (On-RAL), where a robot simultaneously learns object attributes and identifies them, by developing an information-theoretic reward shaping (ITRS) algorithm that balances exploration and exploitation, resulting in improved learning efficiency and identification accuracy compared to baselines.
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.