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Gugliermo, S., Schaffernicht, E., Koniaris, C. & Saffiotti, A. (2023). Extracting Planning Domains from Execution Traces: a Progress Report. In: : . Paper presented at ICAPS 2023, Workshop on Knowledge Engineering for Planning and Scheduling (KEPS 2023), Prague, Czech Republic, July 9-10, 2023.
Open this publication in new window or tab >>Extracting Planning Domains from Execution Traces: a Progress Report
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

One of the difficulties of using AI planners in industrial applications pertains to the complexity of writing planning domain models. These models are typically constructed by domain planning experts and can become increasingly difficult to codify for large applications. In this paper, we describe our ongoing research on a novel approach to automatically learn planning domains from previously executed traces using Behavior Trees as an intermediate human-readable structure. By involving human planning experts in the learning phase, our approach can benefit from their validation. This paper outlines the initial steps we have taken in this research, and presents the challenges we face in the future.

National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-110796 (URN)
Conference
ICAPS 2023, Workshop on Knowledge Engineering for Planning and Scheduling (KEPS 2023), Prague, Czech Republic, July 9-10, 2023
Funder
Swedish Foundation for Strategic Research
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-18Bibliographically approved
Gugliermo, S., Schaffernicht, E., Koniaris, C. & Pecora, F. (2023). Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization. IEEE Robotics and Automation Letters, 8(6), 3534-3541
Open this publication in new window or tab >>Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization
2023 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3534-3541Article in journal (Refereed) Published
Abstract [en]

The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Behavioral sciences, Decision trees, Planning, Batteries, Task analysis, Circuit synthesis, Partitioning algorithms, Behavior-based systems, intelligent transportation systems, learning from demonstration
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-106251 (URN)10.1109/LRA.2023.3268598 (DOI)000981889200003 ()2-s2.0-85153797531 (Scopus ID)
Funder
Swedish Foundation for Strategic Research
Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2024-01-17Bibliographically approved
Kucner, T. P., Magnusson, M., Mghames, S., Palmieri, L., Verdoja, F., Swaminathan, C. S., . . . Lilienthal, A. J. (2023). Survey of maps of dynamics for mobile robots. The international journal of robotics research, 42(11), 977-1006
Open this publication in new window or tab >>Survey of maps of dynamics for mobile robots
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2023 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 42, no 11, p. 977-1006Article in journal (Refereed) Published
Abstract [en]

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
mapping, maps of dynamics, localization and mapping, acceptability and trust, human-robot interaction, human-aware motion planning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-107930 (URN)10.1177/02783649231190428 (DOI)001042374800001 ()2-s2.0-85166946627 (Scopus ID)
Funder
EU, Horizon 2020, 101017274
Note

Funding agencies:

Czech Ministry of Education by OP VVV CZ.02.1.01/0.0/0.0/16 019/0000765

Business Finland 9249/31/2021

 

Available from: 2023-08-30 Created: 2023-08-30 Last updated: 2024-01-03Bibliographically approved
Gutiérrez Maestro, E., Almeida, T. R., Schaffernicht, E. & Martinez Mozos, O. (2023). Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities. Applied Sciences, 13(9), Article ID 5637.
Open this publication in new window or tab >>Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 9, article id 5637Article in journal (Refereed) Published
Abstract [en]

We present a system designed to monitor the well-being of older adults during their daily activities. To automatically detect and classify their emotional state, we collect physiological data through a wearable medical sensor. Ground truth data are obtained using a simple smartphone app that provides ecological momentary assessment (EMA), a method for repeatedly sampling people's current experiences in real time in their natural environments. We are making the resulting dataset publicly available as a benchmark for future comparisons and methods. We are evaluating two feature selection methods to improve classification performance and proposing a feature set that augments and contrasts domain expert knowledge based on time-analysis features. The results demonstrate an improvement in classification accuracy when using the proposed feature selection methods. Furthermore, the feature set we present is better suited for predicting emotional states in a leave-one-day-out experimental setup, as it identifies more patterns.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
activities for daily life (ADL), artificial intelligence, affective computing, machine learning, medical wearable, mental well-being, older adults, smart health
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:oru:diva-106058 (URN)10.3390/app13095637 (DOI)000986950700001 ()2-s2.0-85159278222 (Scopus ID)
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Knut and Alice Wallenberg Foundation
Available from: 2023-05-26 Created: 2023-05-26 Last updated: 2024-01-03Bibliographically approved
Dominguez, D. C., Iannotta, M., Stork, J. A., Schaffernicht, E. & Stoyanov, T. (2022). A Stack-of-Tasks Approach Combined With Behavior Trees: A New Framework for Robot Control. IEEE Robotics and Automation Letters, 7(4), 12110-12117
Open this publication in new window or tab >>A Stack-of-Tasks Approach Combined With Behavior Trees: A New Framework for Robot Control
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2022 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 4, p. 12110-12117Article in journal (Refereed) Published
Abstract [en]

Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this approach solves a sequence of Quadratic Programs (QP) at each time-step, without taking into account any temporal state evolution, it is suitable for dealing with local disturbances. However, its limitation lies in the handling of situations that require non-quadratic objectives to achieve a specific goal, as well as situations where countering the control disturbance would require a locally suboptimal action. Recent works address this shortcoming by exploiting Finite State Machines (FSMs) to compose the tasks in such a way that the robot does not get stuck in local minima. Nevertheless, the intrinsic trade-off between reactivity and modularity that characterizes FSMs makes them impractical for defining reactive behaviors in dynamic environments. In this letter, we combine the SoT control strategy with Behavior Trees (BTs), a task switching structure that addresses some of the limitations of the FSMs in terms of reactivity, modularity and re-usability. Experimental results on a Franka Emika Panda 7-DOF manipulator show the robustness of our framework, that allows the robot to benefit from the reactivity of both SoT and BTs.

Place, publisher, year, edition, pages
IEEE Press, 2022
Keywords
Behavior-based systems, control architectures and programming
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-101946 (URN)10.1109/LRA.2022.3211481 (DOI)000868319800006 ()
Funder
Knut and Alice Wallenberg Foundation
Note

Funding agencies:

Industrial Graduate School Collaborative AI & Robotics (CoAIRob)

General Electric Dnr:20190128

Available from: 2022-10-27 Created: 2022-10-27 Last updated: 2024-01-17Bibliographically approved
Fan, H., Schaffernicht, E. & Lilienthal, A. (2022). Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications. Frontiers in Chemistry, 10, Article ID 863838.
Open this publication in new window or tab >>Ensemble Learning-Based Approach for Gas Detection Using an Electronic Nose in Robotic Applications
2022 (English)In: Frontiers in Chemistry, E-ISSN 2296-2646, Vol. 10, article id 863838Article in journal (Refereed) Published
Abstract [en]

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. A gas detection system is supposed to indicate a significant event, such as the presence of new chemical compounds or a noteworthy change of concentration levels. Existing gas detection methods typically rely on prior knowledge of target analytes to prepare a dedicated, supervised learning model. However, in some scenarios, such as emergency response, not all the analytes of concern are a priori known and their presence are unlikely to be controlled. In this paper, we take a step towards addressing this issue by proposing an ensemble learning-based approach (ELBA) that integrates several one-class classifiers and learns online. The proposed approach is initialized by training several one-class models using clean air only. During the sampling process, the initialized system detects the presence of chemicals, allowing to learn another one-class model and update existing models with self-labelled data. We validated the proposed approach with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an uncontrolled environment. We demonstrated that the ELBA algorithm not only can detect gas exposures but also recognize baseline responses under a suspect short-term sensor drift condition. Depending on the problem setups in practical applications, the present work can be easily hybridized to integrate other supervised learning models when the prior knowledge of target analytes is partially available.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022
Keywords
electronic nose, metal oxide semiconductor sensor, gas detection, gas sensing, open sampling systems, ensemble learning, robotic olfaction
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-98781 (URN)10.3389/fchem.2022.863838 (DOI)000795874900001 ()35572118 (PubMedID)2-s2.0-85130296086 (Scopus ID)
Projects
SmokeBot
Funder
EU, Horizon 2020, 645101
Available from: 2022-04-29 Created: 2022-04-29 Last updated: 2024-01-03Bibliographically approved
Winkler, N. P., Neumann, P. P., Schaffernicht, E., Lilienthal, A., Poikkimäki, M., Kangas, A. & Säämänen, A. (2022). Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory. In: : . Paper presented at 38th Danubia-Adria Symposium on Advances in Experimental Mechanics, Poros Island, Greece, September 20-23, 2022.
Open this publication in new window or tab >>Gather Dust and Get Dusted: Long-Term Drift and Cleaning of Sharp GP2Y1010AU0F Dust Sensor in a Steel Factory
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2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The Sharp GP2Y1010AU0F is a widely used low-cost dust sensor, but despite its popularity, the manufacturer provides little information on the sensor. We installed 16 sensing nodes with Sharp dust sensors in a hot rolling mill of a steel factory. Our analysis shows a clear correlation between sensor drift and accumulated production of the steel factory. An eye should be kept on the long-term drift of the sensors to prevent early saturation. Two of 16 sensors experienced full saturation, each after around eight and ten months of operation.

Keywords
Dust sensor, Low-cost, Sensor drift, Sensor network
National Category
Signal Processing
Identifiers
urn:nbn:se:oru:diva-102768 (URN)
Conference
38th Danubia-Adria Symposium on Advances in Experimental Mechanics, Poros Island, Greece, September 20-23, 2022
Available from: 2022-12-19 Created: 2022-12-19 Last updated: 2024-01-03Bibliographically approved
Iannotta, M., Dominguez, D. C., Stork, J. A., Schaffernicht, E. & Stoyanov, T. (2022). Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees. In: IROS 2022 Workshop on Mobile Manipulation and Embodied Intelligence (MOMA): Challenges and  Opportunities: . Paper presented at International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 23-27, 2022.
Open this publication in new window or tab >>Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees
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2022 (English)In: IROS 2022 Workshop on Mobile Manipulation and Embodied Intelligence (MOMA): Challenges and  Opportunities, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Integrating the heterogeneous controllers of a complex mechanical system, such as a mobile manipulator, within the same structure and in a modular way is still challenging. In this work we extend our framework based on Behavior Trees for the control of a redundant mechanical system to the problem of commanding more complex systems that involve multiple low-level controllers. This allows the integrated systems to achieve non-trivial goals that require coordination among the sub-systems.

National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-102984 (URN)10.48550/arXiv.2210.08600 (DOI)
Conference
International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan, October 23-27, 2022
Funder
Knowledge Foundation
Available from: 2023-01-09 Created: 2023-01-09 Last updated: 2024-01-03Bibliographically approved
Winkler, N. P., Kotlyar, O., Schaffernicht, E., Fan, H., Matsukura, H., Ishida, H., . . . Lilienthal, A. (2022). Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping. In: Danilo Tardioli; Vicente Matellán; Guillermo Heredia; Manuel F. Silva; Lino Marques (Ed.), ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2. Paper presented at ROBOT2022: Fifth Iberian Robotics Conference, Zaragoza, Spain, November 23-25, 2022 (pp. 178-188). Springer, 590
Open this publication in new window or tab >>Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping
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2022 (English)In: ROBOT2022: Fifth Iberian Robotics Conference: Advances in Robotics, Volume 2 / [ed] Danilo Tardioli; Vicente Matellán; Guillermo Heredia; Manuel F. Silva; Lino Marques, Springer, 2022, Vol. 590, p. 178-188Conference paper, Published paper (Refereed)
Abstract [en]

To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.

Place, publisher, year, edition, pages
Springer, 2022
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 590
Keywords
Convolutional LSTM, Gas Distribution Mapping, Sequential Learning, Spatial Interpolation
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-102769 (URN)10.1007/978-3-031-21062-4_15 (DOI)000906176800015 ()2-s2.0-85145267880 (Scopus ID)9783031210617 (ISBN)9783031210624 (ISBN)
Conference
ROBOT2022: Fifth Iberian Robotics Conference, Zaragoza, Spain, November 23-25, 2022
Note

Funding agency:

Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)

Japan Society for the Promotion of Science 22H04952

Available from: 2022-12-19 Created: 2022-12-19 Last updated: 2024-01-03Bibliographically approved
Schindler, M., Doderer, J. H., Simon, A. L., Schaffernicht, E., Lilienthal, A. J. & Schäfer, K. (2022). Small number enumeration processes of deaf or hard-of-hearing students: A study using eye tracking and artificial intelligence. Frontiers in Psychology, 13, Article ID 909775.
Open this publication in new window or tab >>Small number enumeration processes of deaf or hard-of-hearing students: A study using eye tracking and artificial intelligence
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2022 (English)In: Frontiers in Psychology, E-ISSN 1664-1078, Vol. 13, article id 909775Article in journal (Refereed) Published
Abstract [en]

Students who are deaf or hard-of-hearing (DHH) often show significant difficulties in learning mathematics. Previous studies have reported that students who are DHH lag several years behind in their mathematical development compared to hearing students. As possible reasons, limited learning opportunities due to a lesser incidental exposure to numerical ideas, delays in language and speech development, and further idiosyncratic difficulties of students who are DHH are discussed; however, early mathematical skills and their role in mathematical difficulties of students who are DHH are not explored sufficiently. In this study, we investigate whether students who are DHH differ from hearing students in their ability to enumerate small sets (1-9)-an ability that is associated with mathematical difficulties and their emergence. Based on a study with N = 63 who are DHH and N = 164 hearing students from third to fifth grade attempting 36 tasks, we used eye tracking, the recording of students' eye movements, to qualitatively investigate student enumeration processes. To reduce the effort of qualitative analysis of around 8,000 student enumeration processes (227 students x 36 tasks), we used Artificial Intelligence, in particular, a clustering algorithm, to identify student enumeration processes from the heatmaps of student gaze distributions. Based on the clustering, we found that gaze distributions of students who are DHH and students with normal hearing differed significantly on a group level, indicating differences in enumeration processes, with students who are DHH using advantageous processes (e.g., enumeration "at a glance") more often than hearing students. The results indicate that students who are DHH do not lag behind in small number enumeration as compared to hearing students but, rather, appear to perform better than their hearing peers in small number enumeration processes, as well as when conceptual knowledge about the part-whole relationship is involved. Our study suggests that the mathematical difficulties of students who are DHH are not related to difficulties in the small number enumeration, which offers interesting perspectives for further research.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022
Keywords
Artificial Intelligence, deaf or hard-of-hearing students, eye tracking, mathematical difficulties, mathematics education, small number enumeration
National Category
Pedagogy Computer Sciences
Identifiers
urn:nbn:se:oru:diva-101153 (URN)10.3389/fpsyg.2022.909775 (DOI)000849943200001 ()36072043 (PubMedID)2-s2.0-85137910913 (Scopus ID)
Available from: 2022-09-08 Created: 2022-09-08 Last updated: 2024-01-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0804-8637

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