  Assistive robots can potentially improve the quality of life and personal
independence of elderly people by supporting everyday life activities. To
guarantee a safe and intuitive interaction between human and robot, human
intentions need to be recognized automatically. As humans communicate their
intentions multimodally, the use of multiple modalities for intention
recognition may not just increase the robustness against failure of individual
modalities but especially reduce the uncertainty about the intention to be
predicted. This is desirable as particularly in direct interaction between
robots and potentially vulnerable humans a minimal uncertainty about the
situation as well as knowledge about this actual uncertainty is necessary.
Thus, in contrast to existing methods, in this work a new approach for
multimodal intention recognition is introduced that focuses on uncertainty
reduction through classifier fusion. For the four considered modalities speech,
gestures, gaze directions and scene objects individual intention classifiers
are trained, all of which output a probability distribution over all possible
intentions. By combining these output distributions using the Bayesian method
Independent Opinion Pool the uncertainty about the intention to be recognized
can be decreased. The approach is evaluated in a collaborative human-robot
interaction task with a 7-DoF robot arm. The results show that fused
classifiers which combine multiple modalities outperform the respective
individual base classifiers with respect to increased accuracy, robustness, and
reduced uncertainty.
