@inproceedings{
 title = {Robots in the Danger Zone: Exploring Public Perception through Engagement},
 type = {inproceedings},
 year = {2020},
 websites = {https://humanrobotinteraction.org/2020/},
 id = {bf402048-bc67-327a-a426-764d47117a40},
 created = {2021-01-02T16:45:39.239Z},
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 source_type = {conference},
 notes = {15th Annual ACM/IEEE International Conference on Human-Robot Interaction 2020, HRI 2020 ; Conference date: 23-03-2020 Through 26-03-2020},
 private_publication = {false},
 abstract = {Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.},
 bibtype = {inproceedings},
 author = {Robb, David and Ahmad, Muneeb and Tiseo, Carlo and Aracri, Simona and McConnell, Alistair Campbell and Page, Vincent and Dondrup, Christian and Garcia, Francisco Javier Chiyah and Nguyen, Hai-Nguyen and Pairet Artau, Èric and Ardon, Paola and Semwal, Tushar and Taylor, Hazel and Wilson, Lindsay and Lane, David Michael and Hastie, Helen and Lohan, Katrin Solveig},
 booktitle = {15th Annual ACM/IEEE International Conference on Human Robot Interaction}
}

Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

@inproceedings{
 title = {Self-Assessment of Grasp Affordance Transfer},
 type = {inproceedings},
 year = {2020},
 websites = {https://arxiv.org/abs/2007.02132},
 id = {123abe44-fb1e-35ae-a398-524adc5ee6cd},
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 citation_key = {Ardon2020},
 source_type = {CONF},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Ardón, Paola and Pairet, Èric and Petrick, Ron and Ramamoorthy, Subramanian and Lohan, Katrin Solveig},
 booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems  }
}

@inbook{
 type = {inbook},
 year = {2020},
 publisher = {Springer},
 id = {2862ef45-9a09-3524-91ae-91e50a5a7c1a},
 created = {2021-01-02T16:45:39.900Z},
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 bibtype = {inbook},
 author = {Lohan, Katrin Solveig and Ahmad, Muneeb and Dondrup, Christian and Ardon, Paola and Pairet Artau, Èric and Vinciarelli, Alessandro},
 chapter = {Adapting Movements and Behaviour to Favour Communication in Human-Robot Interaction},
 title = {Springer Book on Modelling Human Motion}
}

@inproceedings{
 title = {Towards a Multimodal Measure for Physiological Behaviours to Estimate Cognitive Load.},
 type = {inproceedings},
 year = {2020},
 id = {59e36096-0a03-3ffb-a6c6-01aa30a859e9},
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 bibtype = {inproceedings},
 author = {Ahmad, Muneeb Imtiaz and Robb, David A. and Keller, Ingo and Lohan, Katrin S},
 booktitle = {HCI International 2020}
}

@inproceedings{
 title = {Multimodal Representation Learning for Human Robot Interaction},
 type = {inproceedings},
 year = {2020},
 websites = {https://humanrobotinteraction.org/2020/},
 id = {d603bdba-8e1c-38d4-83c2-7fcb2a5af3db},
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 private_publication = {false},
 bibtype = {inproceedings},
 author = {Sheppard, Eli and Lohan, Katrin Solveig},
 booktitle = {15th Annual ACM/IEEE International Conference on Human Robot Interaction}
}

@inproceedings{
 title = {Social Robot for STEM Education},
 type = {inproceedings},
 year = {2020},
 publisher = {ACM},
 id = {94493d0e-298b-3b8a-babf-5d8e46b9b2cb},
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 citation_key = {467d10df2374421c950701eca733595e},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {This work explores the use of the Cozmo robot to deliver mathematicseducation. Recently, we see a lot of work presented onusing robots for education but few works centred around the agegroup of children between 14-17 years of age in combination witha non-humanoid robot. Reflecting on this limitation, Cozmo autonomouslydelivered engaging material and exercises to learners.We studied the subjective ratings of young learners’ knowledgegains with Cozmo on the topics of algebra, geometry and trigonometry.We found that participants’ subjective rating on their knowledgechanged significantly after the interaction. This implied apositive influence of employing Cozmo with this age group andalso reflected on the need to do more research in schools withaffordable non-humanoid, autonomous social robots.},
 bibtype = {inproceedings},
 author = {Ahmad, Muneeb and Khordi-moodi, Mark and Lohan, Katrin Solveig},
 booktitle = {15th Annual ACM/IEEE International Conference on Human Robot Interaction}
}

This work explores the use of the Cozmo robot to deliver mathematicseducation. Recently, we see a lot of work presented onusing robots for education but few works centred around the agegroup of children between 14-17 years of age in combination witha non-humanoid robot. Reflecting on this limitation, Cozmo autonomouslydelivered engaging material and exercises to learners.We studied the subjective ratings of young learners’ knowledgegains with Cozmo on the topics of algebra, geometry and trigonometry.We found that participants’ subjective rating on their knowledgechanged significantly after the interaction. This implied apositive influence of employing Cozmo with this age group andalso reflected on the need to do more research in schools withaffordable non-humanoid, autonomous social robots.

@article{
 title = {A framework to estimate cognitive load using physiological data},
 type = {article},
 year = {2020},
 keywords = {Cognitive load,Framework,Human-computer interaction,Physiological data},
 id = {48ff30fe-85a5-3ac1-bbd1-44dc39dcf059},
 created = {2021-01-02T18:03:56.313Z},
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 confirmed = {true},
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 citation_key = {Ahmad2020},
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 abstract = {Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.},
 bibtype = {article},
 author = {Ahmad, Muneeb Imtiaz and Keller, Ingo and Robb, David A. and Lohan, Katrin S.},
 doi = {10.1007/s00779-020-01455-7},
 journal = {Personal and Ubiquitous Computing}
}

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.

@article{
 title = {On the Illumination Influence for Object Learning on Robot Companions},
 type = {article},
 year = {2020},
 volume = {6},
 publisher = {Frontiers Media S. A.},
 id = {5476a353-9b8b-34f7-b2d1-fdaa863f59e4},
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 citation_key = {829255f0842e4964be7a7bad805125aa},
 source_type = {article},
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 abstract = {Most collaborative tasks require interaction with everyday objects (e.g., utensils while cooking). Thus, robots must perceive everyday objects in an effective and efficient way. This highlights the necessity of understanding environmental factors and their impact on visual perception, such as illumination changes throughout the day on robotic systems in the real world. In object recognition, two of these factors are changes due to illumination of the scene and differences in the sensors capturing it. In this paper, we will present data augmentations for object recognition that enhance a deep learning architecture. We will show how simple linear and non-linear illumination models and feature concatenation can be used to improve deep learning-based approaches. The aim of this work is to allow for more realistic Human-Robot Interaction scenarios with a small amount of training data in combination with incremental interactive object learning. This will benefit the interaction with the robot to maximize object learning for long-term and location-independent learning in unshaped environments. With our model-based analysis, we showed that changes in illumination affect recognition approaches that use Deep Convolutional Neural Network to encode features for object recognition. Using data augmentation, we were able to show that such a system can be modified toward a more robust recognition without retraining the network. Additionally, we have shown that using simple brightness change models can help to improve the recognition across all training set sizes.},
 bibtype = {article},
 author = {Keller, Ingo and Lohan, Katrin Solveig},
 doi = {10.3389/frobt.2019.00154},
 journal = {Frontiers in Robotics and AI}
}

Most collaborative tasks require interaction with everyday objects (e.g., utensils while cooking). Thus, robots must perceive everyday objects in an effective and efficient way. This highlights the necessity of understanding environmental factors and their impact on visual perception, such as illumination changes throughout the day on robotic systems in the real world. In object recognition, two of these factors are changes due to illumination of the scene and differences in the sensors capturing it. In this paper, we will present data augmentations for object recognition that enhance a deep learning architecture. We will show how simple linear and non-linear illumination models and feature concatenation can be used to improve deep learning-based approaches. The aim of this work is to allow for more realistic Human-Robot Interaction scenarios with a small amount of training data in combination with incremental interactive object learning. This will benefit the interaction with the robot to maximize object learning for long-term and location-independent learning in unshaped environments. With our model-based analysis, we showed that changes in illumination affect recognition approaches that use Deep Convolutional Neural Network to encode features for object recognition. Using data augmentation, we were able to show that such a system can be modified toward a more robust recognition without retraining the network. Additionally, we have shown that using simple brightness change models can help to improve the recognition across all training set sizes.

@article{
 title = {Proceedings of the AI-HRI Symposium at AAAI-FSS 2020},
 type = {article},
 year = {2020},
 volume = {abs/2010.1},
 websites = {https://arxiv.org/abs/2010.13830},
 id = {42256303-4a0b-34f2-adfe-97873af3ccb1},
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 bibtype = {article},
 author = {Bagchi, Shelly and Wilson, Jason R and Ahmad, Muneeb Imtiaz and Dondrup, Christian and Han, Zhao and Hart, Justin W and Leonetti, Matteo and Lohan, Katrin S and Mead, Ross and Senft, Emmanuel and Sinapov, Jivko and Zimmerman, Megan L},
 journal = {CoRR}
}

@article{
 title = {Affordances in Robotic Tasks - A Survey},
 type = {article},
 year = {2020},
 volume = {abs/2004.0},
 websites = {https://arxiv.org/abs/2004.07400},
 id = {878dbe3b-3a0b-3a9f-aa1b-f29625225424},
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 author = {Ardón, Paola and Pairet, Èric and Lohan, Katrin S and Ramamoorthy, Subramanian and Petrick, Ronald P A},
 journal = {CoRR}
}

@inproceedings{
 title = {A Digital Twin for Human-Robot Interaction},
 type = {inproceedings},
 year = {2019},
 pages = {372},
 websites = {https://researchportal.hw.ac.uk/files/24324308/19pairet2019digital.pdf},
 publisher = {IEEE},
 city = {United States},
 series = {ACM/IEEE International Conference on Human-Robot Interaction (HRI)},
 id = {100d31af-e8e7-3b44-95bc-c2360fd8b1f8},
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 citation_key = {b0d8b0eda15840bc9ff0d0a0285b1fbe},
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 abstract = {To avoid putting humans at risk, there is an im-minent need to pursue autonomous robotized facilities withmaintenance capabilities in the energy industry. This paperpresents a video of the ORCA Hub simulator, a frameworkthat unifies three types of autonomous systems (Husky, ANYmaland UAVs) on an offshore platform digital twin for training andtesting human-robot collaboration scenarios, such as inspectionand emergency response.},
 bibtype = {inproceedings},
 author = {Pairet, Èric and Ardon, Paola and Liu, Xingkun and Lopes, Jose and Hastie, Helen and Lohan, Katrin Solveig},
 doi = {10.1109/HRI.2019.8673015},
 booktitle = {2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)}
}

To avoid putting humans at risk, there is an im-minent need to pursue autonomous robotized facilities withmaintenance capabilities in the energy industry. This paperpresents a video of the ORCA Hub simulator, a frameworkthat unifies three types of autonomous systems (Husky, ANYmaland UAVs) on an offshore platform digital twin for training andtesting human-robot collaboration scenarios, such as inspectionand emergency response.

@article{
 title = {Reasoning on Grasp-Action Affordances},
 type = {article},
 year = {2019},
 id = {c44e728f-2eb3-3bc3-91cc-de7214b3a19c},
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 author = {Ardon, Paola and Pairet, Èric and Petrick, Ron and Ramamoorthy, Subramanian and Lohan, Katrin},
 journal = {TAROS}
}

@inproceedings{
 title = {Object Affordances by Inferring on the Surroundings},
 type = {inproceedings},
 year = {2019},
 pages = {69-70},
 websites = {https://researchportal.hw.ac.uk/files/22962004/arso_2018_abstract.pdf},
 publisher = {IEEE},
 city = {United States},
 series = {IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO)},
 id = {5d8b9d84-a9aa-3dee-864e-000b95081f29},
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 abstract = {Robotic cognitive manipulation methods aim to imitate the human-object interactive process. Most of the of the state-of-the-art literature explore these methods by focusing on the target object or on the robot's morphology, without including the surrounding environment. Most recent approaches suggest that taking into account the semantic properties of the surrounding environment improves the object recognition. When it comes to human cognitive development methods, these physical qualities are not only inferred from the object but also from the semantic characteristics of the surroundings. Thus the importance of affordances. In affordances, the representation of the perceived physical qualities of the objects gives valuable information about the possible manipulation actions. Hence, our research pursuits to develop a cognitive affordances map by (i) considering the object and the characteristics of the environment in which this object is more likely to appear, and (ii) achieving a learning mechanism that will intrinsically learn these affordances from self-experience.},
 bibtype = {inproceedings},
 author = {Ardon, Paola and Ramamoorthy, Subramanian and Lohan, Katrin Solveig},
 doi = {10.1109/ARSO.2018.8625829},
 booktitle = {2018 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO)}
}

Robotic cognitive manipulation methods aim to imitate the human-object interactive process. Most of the of the state-of-the-art literature explore these methods by focusing on the target object or on the robot's morphology, without including the surrounding environment. Most recent approaches suggest that taking into account the semantic properties of the surrounding environment improves the object recognition. When it comes to human cognitive development methods, these physical qualities are not only inferred from the object but also from the semantic characteristics of the surroundings. Thus the importance of affordances. In affordances, the representation of the perceived physical qualities of the objects gives valuable information about the possible manipulation actions. Hence, our research pursuits to develop a cognitive affordances map by (i) considering the object and the characteristics of the environment in which this object is more likely to appear, and (ii) achieving a learning mechanism that will intrinsically learn these affordances from self-experience.

@article{
 title = {Learning Grasp Affordance Reasoning through Semantic Relations},
 type = {article},
 year = {2019},
 websites = {http://arxiv.org/abs/1906.09836},
 month = {6},
 day = {24},
 id = {b39c0abe-446b-3e9d-8d7d-53099477ee98},
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 abstract = {Reasoning about object affordances allows an autonomous agent to perform generalised manipulation tasks among object instances. While current approaches to grasp affordance estimation are effective, they are limited to a single hypothesis. We present an approach for detection and extraction of multiple grasp affordances on an object via visual input. We define semantics as a combination of multiple attributes, which yields benefits in terms of generalisation for grasp affordance prediction. We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object. To harvest the knowledge base, we collect and make available a novel dataset that relates different semantic attributes. We achieve reliable mappings of the predicted grasp affordances on the object by learning prototypical grasping patches from several examples. We show our method's generalisation capabilities on grasp affordance prediction for novel instances and compare with similar methods in the literature. Moreover, using a robotic platform, on simulated and real scenarios, we evaluate the success of the grasping task when conditioned on the grasp affordance prediction.},
 bibtype = {article},
 author = {Ardón, Paola and Pairet, Èric and Petrick, Ronald P. A. and Ramamoorthy, Subramanian and Lohan, Katrin S.},
 journal = {Robotics and Automation Letters (RA-L). To be presented at the International Conference on Intelligent Robots and Systems (IROS)}
}

Reasoning about object affordances allows an autonomous agent to perform generalised manipulation tasks among object instances. While current approaches to grasp affordance estimation are effective, they are limited to a single hypothesis. We present an approach for detection and extraction of multiple grasp affordances on an object via visual input. We define semantics as a combination of multiple attributes, which yields benefits in terms of generalisation for grasp affordance prediction. We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object. To harvest the knowledge base, we collect and make available a novel dataset that relates different semantic attributes. We achieve reliable mappings of the predicted grasp affordances on the object by learning prototypical grasping patches from several examples. We show our method's generalisation capabilities on grasp affordance prediction for novel instances and compare with similar methods in the literature. Moreover, using a robotic platform, on simulated and real scenarios, we evaluate the success of the grasping task when conditioned on the grasp affordance prediction.

@article{
 title = {Challenges in Collaborative HRI for Remote Robot Teams},
 type = {article},
 year = {2019},
 keywords = {Robotics,anthropomorphism,conversational agents,embodied,human-robot collaboration,human-robot interaction,human-robot teaming,remote autonomous systems,trust},
 websites = {http://arxiv.org/abs/1905.07379},
 id = {bb8b5845-08a4-30cd-9714-7f9b92a35222},
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 abstract = {Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency response, thus reducing the number of personnel placed in harm's way. As remote robots are generally trusted less than robots in close-proximity, we present a solution to instil trust in the operator through a `mediator robot' that can exhibit social skills, alongside sophisticated visualisation techniques. In this position paper, we present general challenges and then take a closer look at one challenge in particular, discussing an initial study, which investigates the relationship between the level of control the supervisor hands over to the mediator robot and how this affects their trust. We show that the supervisor is more likely to have higher trust overall if their initial experience involves handing over control of the emergency situation to the robotic assistant. We discuss this result, here, as well as other challenges and interaction techniques for human-robot collaboration.},
 bibtype = {article},
 author = {Hastie, Helen and Robb, David A and Lopes, José and Ahmad, Muneeb and Bras, Pierre Le and Liu, Xingkun and Petrick, Ronald P A and Lohan, Katrin and Chantler, Mike J}
}

Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency response, thus reducing the number of personnel placed in harm's way. As remote robots are generally trusted less than robots in close-proximity, we present a solution to instil trust in the operator through a `mediator robot' that can exhibit social skills, alongside sophisticated visualisation techniques. In this position paper, we present general challenges and then take a closer look at one challenge in particular, discussing an initial study, which investigates the relationship between the level of control the supervisor hands over to the mediator robot and how this affects their trust. We show that the supervisor is more likely to have higher trust overall if their initial experience involves handing over control of the emergency situation to the robotic assistant. We discuss this result, here, as well as other challenges and interaction techniques for human-robot collaboration.

@inproceedings{
 title = {Exploring Interaction with Remote Autonomous Systems using Conversational Agents},
 type = {inproceedings},
 year = {2019},
 keywords = {Explainable AI,Multimodal interfaces,Natural Language Interfaces,Remote Autonomous Systems,Transparency,Trust},
 publisher = {ACM},
 id = {6c695dda-35df-31a8-acaa-330b0c411d32},
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 citation_key = {eaa73bdac3274b2fba2423c4b28bb4e8},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Autonomous vehicles and robots are increasingly being deployed to remote, dangerous environments in the energy sector, search and rescue and the military. As a result, there is a need for humans to interact with these robots to monitor their tasks, such as inspecting and repairing offshore wind-turbines. Conversational Agents can improve situation awareness and transparency, while being a hands-free medium to communicate key information quickly and succinctly. As part of our user-centered design of such systems, we conducted an in-depth immersive qualitative study of twelve marine research scientists and engineers, interacting with a prototype Conversational Agent. Our results expose insights into the appropriate content and style for the natural language interaction and, from this study, we derive nine design recommendations to inform future Conversational Agent design for remote autonomous systems.},
 bibtype = {inproceedings},
 author = {Robb, David A and Lopes, Jose and Padilla, Stefano and Laskov, Atanas and Garcia, Francisco Javier Chiyah and Liu, Xingkun and Scharff Willners, Jonatan and Valeyrie, Nicolas and Lohan, Katrin and Lane, David and Patron, Pedro and Petillot, Yvan and Chantler, Michael John and Hastie, Helen},
 booktitle = {DIS '19: Proceedings of the 2019 Designing Interactive Systems Conference}
}

Autonomous vehicles and robots are increasingly being deployed to remote, dangerous environments in the energy sector, search and rescue and the military. As a result, there is a need for humans to interact with these robots to monitor their tasks, such as inspecting and repairing offshore wind-turbines. Conversational Agents can improve situation awareness and transparency, while being a hands-free medium to communicate key information quickly and succinctly. As part of our user-centered design of such systems, we conducted an in-depth immersive qualitative study of twelve marine research scientists and engineers, interacting with a prototype Conversational Agent. Our results expose insights into the appropriate content and style for the natural language interaction and, from this study, we derive nine design recommendations to inform future Conversational Agent design for remote autonomous systems.

@article{
 title = {Trust and Cognitive Load During Human-Robot Interaction},
 type = {article},
 year = {2019},
 keywords = {Cognition,Humanism,Humanities,Humans,Robotics},
 websites = {http://arxiv.org/abs/1909.05160},
 id = {997fa7ca-5f11-3af7-9a0a-5a35ca6e7f62},
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 citation_key = {Ahmad2019},
 source_type = {article},
 notes = {RAYYAN-INCLUSION: "Peter"=>"Included" | RAYYAN-LABELS: cognitive load,anthropomophism},
 private_publication = {false},
 abstract = {This paper presents an exploratory study to understand the relationship between a humans' cognitive load, trust, and anthropomorphism during human-robot interaction. To understand the relationship, we created a $\$sayMatching the Pair game that participants could play collaboratively with one of two robot types, Husky or Pepper. The goal was to understand if humans would trust the robot as a teammate while being in the game-playing situation that demanded a high level of cognitive load. Using a humanoid vs. a technical robot, we also investigated the impact of physical anthropomorphism and we furthermore tested the impact of robot error rate on subsequent judgments and behavior. Our results showed that there was an inversely proportional relationship between trust and cognitive load, suggesting that as the amount of cognitive load increased in the participants, their ratings of trust decreased. We also found a triple interaction impact between robot-type, error-rate and participant's ratings of trust. We found that participants perceived Pepper to be more trustworthy in comparison with the Husky robot after playing the game with both robots under high error-rate condition. On the contrary, Husky was perceived as more trustworthy than Pepper when it was depicted as featuring a low error-rate. Our results are interesting and call further investigation of the impact of physical anthropomorphism in combination with variable error-rates of the robot.},
 bibtype = {article},
 author = {Ahmad, Muneeb Imtiaz and Bernotat, Jasmin and Lohan, Katrin and Eyssel, Friederike}
}

This paper presents an exploratory study to understand the relationship between a humans' cognitive load, trust, and anthropomorphism during human-robot interaction. To understand the relationship, we created a $\$sayMatching the Pair game that participants could play collaboratively with one of two robot types, Husky or Pepper. The goal was to understand if humans would trust the robot as a teammate while being in the game-playing situation that demanded a high level of cognitive load. Using a humanoid vs. a technical robot, we also investigated the impact of physical anthropomorphism and we furthermore tested the impact of robot error rate on subsequent judgments and behavior. Our results showed that there was an inversely proportional relationship between trust and cognitive load, suggesting that as the amount of cognitive load increased in the participants, their ratings of trust decreased. We also found a triple interaction impact between robot-type, error-rate and participant's ratings of trust. We found that participants perceived Pepper to be more trustworthy in comparison with the Husky robot after playing the game with both robots under high error-rate condition. On the contrary, Husky was perceived as more trustworthy than Pepper when it was depicted as featuring a low error-rate. Our results are interesting and call further investigation of the impact of physical anthropomorphism in combination with variable error-rates of the robot.

@inproceedings{
 title = {Integrated real-time, non-intrusive Measurements for Mental Load},
 type = {inproceedings},
 year = {2019},
 websites = {http://matthiasbaldauf.com/automationxp19/papers/AutomationXP19_paper_13.pdf},
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 author = {Ahmad, Muneeb and Keller, Ingo and Lohan, Katrin},
 booktitle = {CHI 2019 Workshop: "Everyday Automation Experience: Non-Expert Users Encountering Ubiquitous Automated Systems"}
}

@inproceedings{
 title = {Introducing a Scalable and Modular Control Framework for Low-cost Monocular Robots in Hazardous Environments},
 type = {inproceedings},
 year = {2019},
 websites = {https://www.iros2019.org/},
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 bibtype = {inproceedings},
 author = {Taylor, Hazel M and Dondrup, Christian and Lohan, Katrin Solveig}
}

@inproceedings{
 title = {Towards a Conversational Agent for Remote Robot-Human Teaming},
 type = {inproceedings},
 year = {2019},
 keywords = {autonomous systems,conversational agent,remote robots},
 pages = {548–549},
 publisher = {IEEE Press},
 series = {HRI ’19},
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 author = {Lopes, José and Robb, David A and Ahmad, Muneeb and Liu, Xingkun and Lohan, Katrin and Hastie, Helen},
 booktitle = {Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction}
}

@inproceedings{
 title = {Multi-Modal Measurements of Mental Load},
 type = {inproceedings},
 year = {2019},
 websites = {https://www.dropbox.com/s/87gvg752ujiaxdo/Poster - Multi-Modal Measurements of Mental Load 2.pdf?dl=0},
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 author = {Keller, Ingo and Ahmad, Muneeb and Lohan, Katrin},
 booktitle = {CHI 2019 Workshop: "The Challenges of Working on Social Robots that Collaborate with People"}
}

@article{
 title = {Reports of the 2018 AAAI Fall Symposium},
 type = {article},
 year = {2019},
 pages = {66-72},
 volume = {40},
 websites = {https://doi.org/10.1609/aimag.v40i2.2887},
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 author = {Adler, Aaron and Dasgupta, Prithviraj and DePalma, Nick and Eslami, Mohammed and Freedman, Richard G and Laird, John E and Lebiere, Christian and Lohan, Katrin S and Mead, Ross and Roberts, Mark and Rosenbloom, Paul S and Senft, Emmanuel and Stein, Frank and Williams, Tom and Wray, Kyle Hollins and Yaman, Fusun and Zilberstein, Shlomo},
 doi = {10.1609/aimag.v40i2.2887},
 journal = {AI Mag.},
 number = {2}
}

@article{
 title = {Proceedings of the AI-HRI Symposium at AAAI-FSS 2019},
 type = {article},
 year = {2019},
 volume = {abs/1909.0},
 websites = {http://arxiv.org/abs/1909.04812},
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 author = {Hart, Justin W and DePalma, Nick and Freedman, Richard G and Iocchi, Luca and Leonetti, Matteo and Lohan, Katrin S and Mead, Ross and Senft, Emmanuel and Sinapov, Jivko and Topp, Elin Anna and Williams, Tom},
 journal = {CoRR}
}

@article{
 title = {Towards improved child robot interaction by understanding eye movements.},
 type = {article},
 year = {2018},
 keywords = {Autism; Avatars; Genetics; Neural networks; Pediat},
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 bibtype = {article},
 author = {Lohan, K S and Sheppard, E and Little, G E and Rajendran, G},
 doi = {10.1109/TCDS.2018.2838342},
 journal = {IEEE Transactions on Cognitive and Developmental Systems}
}

@inproceedings{
 title = {Towards robust grasps: Using the environment semantics for robotic object affordances},
 type = {inproceedings},
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 notes = {AAAI 2018 Fall Symposium : Reasoning and Learning in Real World Systems for Long-term Autonomy ; Conference date: 18-10-2018 Through 20-10-2018},
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 bibtype = {inproceedings},
 author = {Ardon Ramirez, Paola and Pairet Artau, Èric and Ramamoorthy, Subramanian and Lohan, Katrin Solveig}
}

@inproceedings{
 title = {Symptoms of Cognitive Load in Interactions with a Dialogue System},
 type = {inproceedings},
 year = {2018},
 keywords = {cognitive load,multi-modal systems,spoken dialogue systems},
 websites = {https://doi.org/10.1145/3279810.3279851},
 publisher = {Association for Computing Machinery},
 city = {New York, NY, USA},
 series = {MCPMD ’18},
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 doi = {10.1145/3279810.3279851},
 booktitle = {Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data}
}

@inproceedings{
 title = {How can we transition from lab to the real world with our HCI and HRI setups?},
 type = {inproceedings},
 year = {2018},
 keywords = {Design,Human computer interaction,Performance,Reliability,human robot interaction},
 id = {47fc60fd-667d-3094-8976-1a02a09edbd7},
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 abstract = {In this position paper, we present the issues we and others have found when moving from the controlled lab space into the field. We therefore recommend some dos and don'ts for facing the challenge of transferring your research prototype from the lab to the real world. During this transfer, we often encounter crucial disconnects between our envisaged evaluation protocol and real life, often related to differing user expectations outside controlled experimental interactions. The redeployment of complex systems in unfamiliar (and often dissimilar) environments presents additional challenges.In this paper, we present some example transitions in the fields of mobile HCI and HRI. Based on these experiences, we list the possible roadblocks that other researchers might encounter and provide guidelines and suggestions for dealing with frequently encountered issues. We hope that with this paper we can help stimulate the discussion on the pathway to undertaking scientifically reproducible evaluations in the wild.},
 bibtype = {inproceedings},
 author = {Dondrup, Christian and Baillie, Lynne and Lohan, Katrin Solveig and Broz, Frank},
 booktitle = {The 4th Workshop on Public Space Human-Robot Interaction}
}

In this position paper, we present the issues we and others have found when moving from the controlled lab space into the field. We therefore recommend some dos and don'ts for facing the challenge of transferring your research prototype from the lab to the real world. During this transfer, we often encounter crucial disconnects between our envisaged evaluation protocol and real life, often related to differing user expectations outside controlled experimental interactions. The redeployment of complex systems in unfamiliar (and often dissimilar) environments presents additional challenges.In this paper, we present some example transitions in the fields of mobile HCI and HRI. Based on these experiences, we list the possible roadblocks that other researchers might encounter and provide guidelines and suggestions for dealing with frequently encountered issues. We hope that with this paper we can help stimulate the discussion on the pathway to undertaking scientifically reproducible evaluations in the wild.

@article{
 title = {The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces},
 type = {article},
 year = {2018},
 pages = {1-2},
 websites = {http://arxiv.org/abs/1803.02100},
 id = {fef9e0d5-696e-303f-8a08-43115c51131a},
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 abstract = {We present the UK Robotics and Artificial Intelligence Hub for Offshore Robotics for Certification of Assets (ORCA Hub), a 3.5 year EPSRC funded, multi-site project. The ORCA Hub vision is to use teams of robots and autonomous intelligent systems (AIS) to work on offshore energy platforms to enable cheaper, safer and more efficient working practices. The ORCA Hub will research, integrate, validate and deploy remote AIS solutions that can operate with existing and future offshore energy assets and sensors, interacting safely in autonomous or semi-autonomous modes in complex and cluttered environments, co-operating with remote operators. The goal is that through the use of such robotic systems offshore, the need for personnel will decrease. To enable this to happen, the remote operator will need a high level of situation awareness and key to this is the transparency of what the autonomous systems are doing and why. This increased transparency will facilitate a trusting relationship, which is particularly key in high-stakes, hazardous situations.},
 bibtype = {article},
 author = {Hastie, Helen and Lohan, Katrin and Chantler, Mike and Robb, David A. and Ramamoorthy, Subramanian and Petrick, Ron and Vijayakumar, Sethu and Lane, David}
}

We present the UK Robotics and Artificial Intelligence Hub for Offshore Robotics for Certification of Assets (ORCA Hub), a 3.5 year EPSRC funded, multi-site project. The ORCA Hub vision is to use teams of robots and autonomous intelligent systems (AIS) to work on offshore energy platforms to enable cheaper, safer and more efficient working practices. The ORCA Hub will research, integrate, validate and deploy remote AIS solutions that can operate with existing and future offshore energy assets and sensors, interacting safely in autonomous or semi-autonomous modes in complex and cluttered environments, co-operating with remote operators. The goal is that through the use of such robotic systems offshore, the need for personnel will decrease. To enable this to happen, the remote operator will need a high level of situation awareness and key to this is the transparency of what the autonomous systems are doing and why. This increased transparency will facilitate a trusting relationship, which is particularly key in high-stakes, hazardous situations.

@inproceedings{
 title = {Playing Pairs with Pepper},
 type = {inproceedings},
 year = {2018},
 websites = {http://ai-hri.github.io/2018/},
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 author = {Yaseen, Abdelrahman and Lohan, Katrin Solveig}
}

@inproceedings{
 title = {Using Pupil Diameter to Measure Cognitive Load},
 type = {inproceedings},
 year = {2018},
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 author = {Minadakis, Georgios and Lohan, Katrin},
 booktitle = {arXiv preprint arXiv:1812.07653}
}

@article{
 title = {Social Impact of recharging activity in long-term HRI and verbal strategies to manage user expectations during recharge},
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Social robots perform tasks to help humans in their daily activities. However, if they fail to fulfill expectations this may affect their acceptance. This work investigates the service degradation caused by recharging, during which the robot is socially inactive. We describe two studies conducted in an ecologically valid office environment. In the first long-term study (3 weeks), we investigated the service degradation caused by the recharging behavior of a social robot. In the second study, we explored the social strategies used to manage users� expectations during recharge. Our findings suggest that the use of verbal strategies (transparency, apology, and politeness) can make robots more acceptable to users during recharge.

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@inproceedings{
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@inproceedings{
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@inproceedings{
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In this article we evaluate the incremental object learning approach of the iCub humanoid robot which is directed towards long-term engagement. Affordable robot companion systems are currently entering the consumer market which highlights the importance in understanding environmental influences on robotic systems under real world conditions. If a robot is to be sent into the real world or different robots/sensors are to be used, we need our algorithms to be independent from both illumination and sensor influenced changes. In our work, we investigate the robustness of the interactive object learning to linear and non-linear lighting changes which can occur due to illumination changes throughout the day or the sensors used. Our results with the models we use suggest that the current method is susceptible to these changes. Therefore, we provide an adjustment to the current method to be able to cope with this problem.

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@inproceedings{
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@inproceedings{
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@inproceedings{
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@inproceedings{
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@inproceedings{
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@inproceedings{
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@inproceedings{
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@inproceedings{
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@article{
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