Multimodal Uncertainty Reduction for Intention Recognition in HumanRobot Interaction 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 recognized. 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 1 the uncertainty about the intention to be recognized can be decreased. The approach is evaluated in a collaborative humanrobot interaction task with a 7DoF 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.

Inference of userintention in remote robot wheelchair assistance using multimodal interfaces Shared control methodologies have the potential of enabling wheelchairbound users with limited motor abilities to perform tasks that would usually be beyond their capabilities. Deriving such methodologies in advance is challenging, since they are frequently heavily dependent on unique characteristics of users. Learning Assistance by Demonstration paradigms allow derivation of customized policies by recording how remote human assistants assist particular users. However, for accurate determination of the optimal policies for each user and context, the remote assistant needs to infer the intention of the driver, which is frequently obscured by noisy signals dependent on the userxe2x80x99s motor impairment. In this paper, we propose a multimodal teleoperation interface, incorporating map information, haptic feedback and user eyegaze data, and examine which of these factors are most important for allowing accurate determination of user intention in a simulated tremor experiment. Our study indicates that, for expert assistants, presence of additional haptic and gaze information increases their ability to accurately infer the userxe2x80x99s intention, providing supporting evidence for the utility of multimodal interfaces in remote assistance scenarios for Learning Assistance by Demonstration. Our study also reveals strong individual preferences on the different modalities, with large variations of performance occurring depending on whether supplemental eyegaze or haptic information was given.