Assisting with Goal Formulation for Domain Independent Planning

Göbelbecker, Moritz

doi:10.1007/978-3-319-24489-1_7

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Objects or sets of objects are identified by creating referring expressions to them composed of shared references built on this vocabluary [18]. We briefly describe the relevant aspects of our previous work in this area [19]. In general, a referring expression φ is a logical formula with a single free variable.…”

Section: High-level Goal Formulation Planningmentioning

confidence: 99%

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Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Burget

Fiederer

Kuhner

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

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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.

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Section: High-level Goal Formulation Planningmentioning

confidence: 99%

“…(a) SNR of the first 4 s of data used to train the hybrid ConvNet. Highest SNR can be observed in the alpha (7-14 Hz) and lower beta(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26) bands. These frequency bands are robust markers of task related mental tasks.…”

mentioning

confidence: 99%

Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills

Burget

Fiederer

Kuhner

et al. 2017

2017 European Conference on Mobile Robots (ECMR)

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“…To solve this problem, the goal generation and selection component uses a set of basic references shared between planner and user. These shared references can be combined to create referring expressions to objects or sets of objects in the world [40, 41]. Generally, a referring expression is a logical formula with a single free variable.…”

Section: Autonomous Bci-controlled Service Assistantmentioning

confidence: 99%

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

Kuhner

Fiederer

Aldinger

et al. 2018

Preprint

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As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. 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 system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

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