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.

Conf. on Information and Knowledge Management (CIKM) h5 49 International Conference on Artificial Intelligence and Statistics (AISTATS) h5 43 Data Mining and Knowledge Discovery Journal (DMKD) h5 35 Neural Computation h5 34 SIAM International Conference on Data Mining (SDM) h5 33 European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD) h5 30 European Conference on Information Retrieval (ECIR) h5 26 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) h5 23 CORE B ACM Conference on Recommender Systems (RecSys) h5 40 International Joint Conference on Neural Networks (IJCNN) h5 32 Neural Processing Letters h5 23 Information Retrieval h5 20 International Conference on Artificial Neural Networks (ICANN) h5 14 International Conference in Inductive Logic Programming (ILP) h5 - CORE Unranked Asian Conference on Machine Learning (ACML) h5 13 International Conference on Learning Representations (ICLR) h5 - Wahrnehmung und Sehen Die Fähigkeit zur Verarbeitung visueller Information ist eine Grundbedingung für künstliche Intelligenzen.

We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is to formalise the problem as statistical preference elicitation, via a number of structured priors, whose form captures our biases about the relatedness of different tasks or expert policies. In doing so, we introduce a prior on policy optimality, which is more natural to specify. We show that our framework allows us not only to learn to efficiently from multiple experts but to also effectively differentiate between the goals of each. Possible applications include analysing the intrinsic motivations of subjects in behavioural experiments and learning from multiple teachers.

We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent's preferences, policy and optionally, the obtained reward sequence, from observations. We examine the relation of the resulting approach to other statistical methods for inverse reinforcement learning via analysis and experimental results. We show that preferences can be determined accurately, even if the observed agent's policy is sub-optimal with respect to its own preferences. In that case, significantly improved policies with respect to the agent's preferences are obtained, compared to both other methods and to the performance of the demonstrated policy.