No abstract

As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic system. Currently available control modalities typically expect users to be able to express their desire through either touch, speech or gesture commands. While this requirement is fulfilled for the majority of users, paralyzed users may not be able to use such systems. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The brain-computer interface (BCI) system is composed of several interacting components, i.e., non-invasive neuronal signal recording and decoding, high-level task planning, motion and manipulation planning as well as environment perception.In various experiments, we demonstrate its applicability and robustness in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time. Combined with high-level planning and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based humanrobot interactions.

Abstract-Being able to estimate the dirt distribution in an environment makes it possible to compute efficient cleaning paths for robotic cleaners. In this paper, we present a novel approach for modeling and estimating the dynamics of the dirt generation in an environment. Our model uses cell-wise Poisson processes on a regular grid to represent the dirt in the environment, which allows for an effective estimation of the dynamics of the dirt generation and for making predictions about the absolute dirt values. We propose two efficient cleaning policies which are based on the estimated dirt distributions and can easily be adapted to different needs of potential users. In extensive experiments carried out in simulation and with a modified iRobot Roomba vacuum cleaning robot, we demonstrate the effectiveness of our approach.