Johannes Aldinger – Publications

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2019

Daniel Kuhner, Lukas D.J. Fiederer, Johannes Aldinger, Felix Burget, Martin Völker, Robin T. Schirrmeister, Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball and Wolfram Burgard.
A service assistant combining autonomous robotics, flexible goal formulation, and deep-learning-based brain–computer interfacing.
Robotics and Autonomous Systems 116, pp. 98-113. 2019.
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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 robotic tasks and environments. Traditional control modalities such as touch, speech or gesture are not necessarily suited for all users. While some users can make the effort to familiarize themselves with a robotic system, users with motor disabilities 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: a brain–computer interface (BCI) that uses non-invasive neuronal signal recording and co-adaptive deep learning, 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, close human–robot interactions and in presence of unexpected changes. 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 task planning based on referring expressions and an autonomous robotic system, interesting new perspectives open up for non-invasive BCI-based human–robot interactions.

2018

Daniel Kuhner, Johannes Aldinger, Felix Burget, Moritz Göbelbecker, Wolfram Burgard and Bernhard Nebel.
Closed-Loop Robot Task Planning Based on Referring Expressions.
In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), pp. 876-881. 2018.
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Increasing the accessibility of autonomous robots also for inexperienced users requires user-friendly and high-level control opportunities of robotic systems. While automated planning is able to decompose a complex task into a sequence of steps which reaches an intended goal, it is difficult to formulate such a goal without knowing the internals of the planning system and the exact capabilities of the robot. This becomes even more important in dynamic environments in which manipulable objects are subject to change. In this paper, we present an adaptive control interface which allows users to specify goals based on an internal world model by incrementally building referring expressions to the objects in the world. We consider fetch-and-carry tasks and automatically deduce potential high-level goals from the world model to make them available to the user. Based on its perceptions our system can react to changes in the environment by adapting the goal formulation within the domain-independent planning system.

2017

Johannes Aldinger and Bernhard Nebel.
Addendum to 'Interval Based Relaxation Heuristics for Numeric Planning with Action Costs'.
Technical Report 280, Institut für Informatik, Albert-Ludwigs-Universität Freiburg, 2017.
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This report contains two theorems and their proofs supplementing our work published at the 40th German Conference on Artificial Intelligence under the title 'Interval Based Relaxation Heuristics for Numeric Planning with Action Costs'.
Johannes Aldinger and Bernhard Nebel.
Interval Based Relaxation Heuristics for Numeric Planning with Action Costs.
In KI 2017:Advances in Artificial Intelligence (KI 2017), pp. 15-28. Springer International Publishing 2017.
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Many real-world problems can be expressed in terms of states and actions that modify the world to reach a certain goal. Such problems can be solved by automated planning. Numeric planning supports numeric quantities such as resources or physical properties in addition to the propositional variables from classical planning. We approach numeric planning with heuristic search and introduce adaptations of the relaxation heuristics h_max, h_add and h_FF to interval based relaxation frameworks. In contrast to previous approaches, the heuristics presented in this paper are not limited to fragments of numeric planning with instantaneous actions (such as linear or acyclic numeric planning tasks) and support action costs.
F. Burget, L.D.J. Fiederer, D.Kuhner, M.Völker, Johannes Aldinger, R.T. Schirrmeister, C.Do, J.Boedecker, Bernhard Nebel, T.Ball and W.Burgard.
Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills.
In Proceedings of the European Conference on Mobile Robotics (ECMR 2017), pp. 385-390. 2017.
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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 human-robot interactions.
Johannes Aldinger and Bernhard Nebel.
Extended Abstract: Interval Based Relaxation Heuristics for Numeric Planning with Action Costs.
In Proceedings of the 10th International Symposium on Combinatorial Search (SoCS 2017), pp. 155-156. 2017.
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We adapt the relaxation heuristics h_max, h_add and h_FF to interval based numeric relaxation frameworks, combining them with two different relaxation techniques and with two different search techniques. In contrast to previous approaches, the heuristics presented here are not limited to a subset of numeric planning and support action costs.

2016

Johannes Aldinger and Johannes Löhr.
The Jumpbot Domain for Numeric Planning.
Technical Report 279, Institut für Informatik, Albert-Ludwigs-Universität Freiburg, 2016.
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The Jumpbot domain for numeric planning with instantaneous actions models a walking robot that has to reach a target region by jumping over water ditches. The kinematic of the robot is modeled by its current position and velocity vector. The planner has to reason about the correct accelerations, rotations, velocities, jump positions and space for the deceleration. This domain description provides a set of benchmark instances for numeric planning in an area which is underrepresented by prevailing benchmarks: the use of numeric variables to model physical properties as opposed to their use for modeling resources.

2015

Johannes Aldinger, Robert Mattmüller and Moritz Göbelbecker.
Complexity Issues of Interval Relaxed Numeric Planning.
In KI 2015: Advances in Artificial Intelligence (KI 2015). 2015.
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Automated planning is computationally hard even in its most basic form as STRIPS planning. We are interested in numeric planning with instantaneous actions, a problem that is not decidable in general. Relaxation is an approach to simplifying complex problems in order to obtain guidance in the original problem. We present a relaxation approach with intervals for numeric planning and show that plan existence can be decided in polynomial time for tasks where dependencies between numeric effects are acyclic.
Johannes Aldinger, Robert Mattmüller and Moritz Göbelbecker.
Complexity Issues of Interval Relaxed Numeric Planning.
In Proceedings of the ICAPS-2015 Workshop on Heuristic and Search for Domain-Independent Planning (HSDIP 2015). 2015.
Superseded by the KI 2015 paper of the same name..
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Automated planning is a hard problem even in its most basic form as STRIPS planning. We are interested in numeric planning tasks with instantaneous actions, a problem which is not even decidable in general. Relaxation is an approach to simplifying complex problems in order to obtain guidance in the original problem. In this paper we present a relaxation approach with intervals for numeric planning and discuss the arising complexity issues.

2013

Johannes Löhr, Johannes Aldinger, Stefan Winkler and Georg Willich.
Automated Planning for Earth Observation Spacecraft under Attitude Dynamical Constraints.
In Jahrbuch der Deutschen Gesellschaft für Luft- und Raumfahrt (DGLR2013). 2013.
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Agile Earth observation missions continuously require a large amount of planning during the spacecraft's observations. Beside priorities of the observation sites, especially the agility constraints of the satellite are important to be taken into account during the planning process. This is due to the body-fixed instrument's line of sight, requiring the whole satellite to point to the observation sites while scanning. Scanning a sequence of observation sites leads to complex slew maneuvers which must not exceed the satellite's actuator capacities, attitude constraints or maximum angular rates. Additionally, the regions of interest may change over time, making it necessary to adapt and optimize the observation sequence continuously. An automated process is required to efficiently handle this task. We present a planning algorithm to sequence an arbitrarily distributed set of observation patches to a feasible observation plan, considering priority criteria of the observation sites and agility constraints of the satellite.
Johannes Aldinger and Johannes Löhr.
Planning for Agile Earth Observation Satellites.
In Proceedings of the ICAPS-2013 Workshop on Planning in Continuous Domains (PCD), pp. 9-17. 2013.
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Agile Earth observation satellites are satellites orbiting Earth with the purpose to gather information of the Earth's surface by slewing the satellite toward regions of interest. Constraints arise not only from dynamical and kinematic aspects of the satellite and its sensors. Regions of interest change over time and bad weather can conceal important observation targets. This results in a constant need to replan the satellite's tasks and raises the desire to automatize this planning process. We consider the Earth observation problem with the help of the module extension of the numerical planning system Temporal Fast Downward. Complex satellite slew maneuvers are calculated within modules, while the planner selects and schedules the regions to be scanned. First results encourage deeper research in this area so that forthcoming satellite space missions can draw on automated planning to improve the performance of agile Earth observation tasks.