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Exploring Human-Co-Robot Interactions: Real-time Feedback or not?

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2018 ASEE Mid-Atlantic Section Spring Conference


Washington, District of Columbia

Publication Date

April 6, 2018

Start Date

April 6, 2018

End Date

April 7, 2018

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Paper Authors


Christian Enmanuel Lopez Pennsylvania State University, University Park Orcid 16x16

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Christian Lopez Bencosme, is currently a Ph.D. student at Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at the Pennsylvania State University. He has worked as an Industrial Engineer in both the Service and Manufacturing sectors before pursuing his Ph.D. His current research focused on the design and optimization of systems and intelligent assistive technologies through the acquisition, integration, and mining of large scale, disparate data. He is currently working on a project that ambition to design a system capable of providing students customized motivational stimuli and performance feedback based on their affective states.

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Conrad Tucker Pennsylvania State University, University Park

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Dr. Tucker holds a joint appointment as Assistant Professor in Engineering Design and Industrial Engineering at The Pennsylvania State University. He is also affiliate faculty in Computer Science and Engineering. He teaches Introduction to Engineering Design (EDSGN 100) at the undergraduate level and developed and taught a graduate-level course titled Data Mining–Driven Design (EDSGN 561). As part of the Engineering Design Program’s “Summers by Design” (SBD) program, Dr. Tucker supervises students from Penn State during the summer semester in a two-week engineering design program at the École Centrale de Nantes in Nantes, France.

Dr. Tucker is the director of the Design Analysis Technology Advancement (D.A.T.A) Laboratory. His research interests are in formalizing system design processes under the paradigm of knowledge discovery, optimization, data mining, and informatics. His research interests include applications in complex systems design and operation, product portfolio/family design, and sustainable system design optimization in the areas of engineering education, energy generation systems, consumer electronics, environment, and national security.

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Appropriate and timely feedback has the potential to improve the learning process. In traditional learning environments, instructors can provide appropriate and timely feedback to students based on their performance and affective states. However, this type of feedback is challenging to provide when the student to instructor ratio high, such as in Engineering Lab environments. Hence, researchers have started exploring how intelligent systems, such as Collaborative-Robots (i.e., Co-Robots), can be implemented in learning environments to assist students towards the successful completion of a task by providing real-time performance feedback. Nonetheless, the type of feedback provided to students (e.g., positive or negative) can have a direct impact on their affective state and ultimately, their performance on tasks. Hence, providing real-time feedback to students may have a detrimental effect on their performance, if that feedback is not delivered at the appropriate time. Although researchers are making significant advances in improving human-co-robot interactions, determining when to provide feedback that advances students’ learning remains an open research question. In light of this, the authors explored the effects that different types of real-time feedback (i.e., positive and negative) have on students’ performance. Similarly, the effects of not providing real-time feedback are investigated. Furthermore, this work explores how students’ facial expression can be captured by Co-robot systems to better understand when to provide real-time performance feedback to students. This work contributes to advancing the field of human-co-robot interactions as well as the National Academy of Engineering’s grand challenge of personalized learning by demonstrating how a student’s facial expression can be employed by Co-robot systems to provide feedback that improves his/her performance.

Lopez, C. E., & Tucker, C. (2018, April), Exploring Human-Co-Robot Interactions: Real-time Feedback or not? Paper presented at 2018 ASEE Mid-Atlantic Section Spring Conference, Washington, District of Columbia. 10.18260/1-2--29462

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