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Conference Papers Year : 2011

Towards Expectation-based Failure Recognition for Human Robot Interaction


With robots becoming increasingly autonomous helpers in human environments, recent development goes from semi-autonomous robotic systems , that need to be directed, towards autonomous partners that can cooperate with humans in joint activities. When operating autonomously in the real world, the probability of unexpected events dramatically increases and failures might make it necessary for the robot to adapt its behavior to be able to fulfill its goals. In our complex and constantly changing real world, it is not possible for the programmer to anticipate all situations a robot might encounter. So the diagnosis of cooperative plans for human robot interaction is a particular challenge since for a robot it is often unclear what is to be considered as an error. A general understanding of normality, based on the validity expectations would enable a robot to detect unexpected events and failures that have not been foreseen by the programmer, thus leading to a more robust and flexible behavior of the robot. We propose the combination of different learned models and common-sense knowledge to generate expectations, that could improve failure detection and enable us to detect and react upon unexpected events. In this paper, we formulate the key challenges for failure detection in human robot interaction, which we see in the representation of expectations, the modeling efficiency and the execution efficiency. We also provide a stack of possible knowledge-based solutions.
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hal-01571935 , version 1 (04-08-2017)


  • HAL Id : hal-01571935 , version 1


Michael Karg, Martin Sachenbacher, Alexandra Kirsch. Towards Expectation-based Failure Recognition for Human Robot Interaction. 22nd International Workshop on Principles of Diagnosis, Special Track on Open Problem Descriptions, Oct 2011, Murnau, Germany. ⟨hal-01571935⟩
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