What Can I Help You With: Towards Task-Independent Detection of Intentions for Interaction in a Human-Robot Environment

Assistive robots interacting with people promise to increase quality of life and productivity in households, caregiving, or industry settings. Importantly, the quality of such interactions crucially depends on the intuitive ease and reliability of humans being able to request the robot's assistance. Thus, the ability to detect a human's Intention for Interaction (IFI) is beneficial for human-robot interaction across multiple application domains. However, existing works that detect IFIs often focus on single tasks, contexts, or interactions or limit their data collection to invariability in human positions. In contrast, here we aim for a more task-independent IFI detection. We record natural human behavior in an experimental setup with a two-armed robot that includes different tasks and interactions, and different positions and orientations of the human towards the robot. We collected audio and RGB-D data from 21 human subjects in the proposed experimental setup resulting in overall 405 IFIs. Using head orientation, shoulder orientation, distance, speech activity recognition, and hotword detection as features, we trained multimodal probabilistic classifiers. We compare feature fusion and decision fusion using the Bayesian fusion method Independent Opinion Pool. The resulting multimodal classifiers can detect task-independent IFIs from natural human behavior with an F1 score of up to 0.81. Overall, we show that good IFI detection can be achieved by modularly combining individual classifiers probabilistically.