Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot 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 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.Comment: Submitted to IROS 201

Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot 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 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

Context change and triggers for human intention recognition

In human-robot interaction, understanding human intention is important to smooth interaction between humans and robots. Proactive human-robot interactions are the trend. They rely on recognising human intentions to complete tasks. The reasoning is accomplished based on the current human state, environment and context, and human intention recognition and prediction. Many factors may affect human intention, including clues which are difficult to recognise directly from the action but may be perceived from the change in the environment or context. The changes that affect human intention are the triggers and serve as strong evidence for identifying human intention. Therefore, detecting such changes and identifying such triggers are the promising approach to assist in human intention recognition. This paper discusses the current state of art in human intention recognition in human-computer interaction and illustrates the importance of context change and triggers for human intention recognition in a variety of examples