LCSR Seminar: Student Seminars
Ulas Berk Karli and Shiye (Sally) Cao “What if it is wrong: effects of power dynamics and trust repair strategy on trust and compliance in HRI.”
Abstract: Robotic systems designed to work alongside people are susceptible to technical and unexpected errors. Prior work has investigated a variety of strategies aimed at repairing people’s trust in the robot after its erroneous operations. In this work, we explore the effect of post-error trust repair strategies (promise and explanation) on people’s trust in the robot under varying power dynamics (supervisor and subordinate robot). Our results show that, regardless of the power dynamics, promise is more effective at repairing user trust than explanation. Moreover, people found a supervisor robot with verbal trust repair to be more trustworthy than a subordinate robot with verbal trust repair. Our results further reveal that people are prone to complying with the supervisor robot even if it is wrong. We discuss the ethical concerns in the use of supervisor robot and potential interventions to prevent improper compliance in users for more productive human-robot collaboration.
Bio: Ulas Berk Karli is a MSE student in Robotics LCSR, Johns Hopkins University. He received the Bachelor of Science degree in Mechanical Engineering and Double Majored in Computer Engineering from Koc University, Istanbul in 2021. His research interests are Human-Robot Collaboration and Robot Learning for HRI.
Shiye Cao is a first-year Ph.D. student in the Department of Computer Science, co-advised by Dr. Chien-Ming Huang and Dr. Anqi Liu. She received the Bachelor of Science degree in Computer Science with a second major in Applied Mathematics and Statistics from Johns Hopkins University in 2021, and the Masters of Science in Engineering in Computer Science from Johns Hopkins University in 2022. Her work focuses on user trust and reliance in human-machine collaborative tasks.
Eugene Lin “Robophysical modeling of spider vibration sensing of prey on orb webs”
Abstract: Orb-weaving spiders are functionally blind and detect prey-generated web vibrations through vibration sensors at their leg joints to locate and identify prey caught in their (near) planar webs. Previous studies focused on how spiders use web geometry, silk properties, and web pre-tension to modulate vibration sensing. Spiders can also dynamically adjust their posture while sensing prey, which may be a form of active sensing (Hung, Corver, Gordus, 2022, APS March Meeting). However, whether this is true and how it works is poorly understood, due to difficulty of measuring the dynamics of the entire prey-web-spider interaction system all at once. Here, we developed a robophysical model of the system to test this hypothesis of active sensing and discover its principles. Our model consists of a vibrating prey robot and a spider robot that can adjust its posture, with torsional springs at leg joints and accelerometers to measure joint vibration. Both robots are attached to a physical web made of cords with qualitatively similar properties to real spider web threads. Load cells measure web pre-tension and a high-speed camera system measure web vibrations and robot movement. Preliminary results showed vibration attenuation through the web from the prey robot. We are currently studying the complex effects of spider robot’s dynamic posture change on vibration propagation across the web and leg joints, by systematically varying the parameters of prey robot vibration, spider robot leg posture, and web pre-tension.
Bio: Eugene Lin is a third year PhD student in Dr. Chen Li’s lab (Terradynamics lab). His work focuses on understanding environmental sensing on suspended, sparse terrain. He received a B.S. in Mechanical Engineering at the University of California, San Diego. He recently presented this work at the annual SICB conference and will present it again at the annual March APS conference.
Aishwarya Pantula “Pick a Side: Untethered Gel Crawlers That Can Break Symmetry”
Abstract: The development of untethered soft crawling robots programmed to respond to environmental stimuli and precisely maneuverable across size scales has been paramount to the fields of soft robotics, drug delivery, and autonomous smart devices. Of particular relevance are reversible thermoresponsive hydrogels, which swell and shrink in the temperature range of (30- 60 °C) for operating such untethered soft robots in human physiological and ambient conditions. While crawling has been demonstrated by thermoresponsive hydrogels, they need surface modifications in the form of rachets, asymmetric patterning, or constraints to achieve unidirectional motion.
Here we demonstrate and validate a new mechanism for untethered, unidirectional crawling for multisegmented gel crawlers built from an active thermoresponsive poly (N-isopropyl acrylamide) (pNIPAM) and passive polyacrylamide (pAAM) on flat unpatterned surfaces. By connecting bilayers of different geometries and thicknesses using a centrally suspended gel linker, we create a morphological gradient along the fore-aft axis, which leads to an asymmetry in the contact forces during the swelling and deswelling of our crawler. We thoroughly explain our mechanism using experiments and finite element simulations and, using experiments, demonstrate that we can tune the generated asymmetry and, in turn, increase the displacement of the crawler by varying linker stiffness, morphology, and the number of bilayer segments. We believe this mechanism can be widely applied across fields of study to create the next generation of autonomous shape-changing and smart locomotors.
Bio: Aishwarya is a 4th year Ph.D. candidate in the lab of Dr. David Gracias at Johns Hopkins University, USA. Her research focuses on exploring smart materials like stimuli-responsive hydrogels, combining them with novel patterning methods like 3D/4D printing, imprint molding, lithography, etc., and using different mechanical design strategies to create untethered biomimetic actuators and locomotors across size scales for soft robotics and biomedical devices.
Maia Stiber “On using social signals to enable flexible error-aware HRI.”
Abstract: Prior error management techniques often do not possess the versatility to appropriately address robot errors across tasks and scenarios. Their fundamental framework involves explicit, manual error management and implicit domain-specific information driven error management, tailoring their response for specific interaction contexts. We present a framework for approaching error-aware systems by adding implicit social signals as another information channel to create more flexibility in application. To support this notion, we introduce a novel dataset (composed of three data collections) with a focus on understanding natural facial action unit (AU) responses to robot errors during physical-based human-robot interactions—varying across task, error, people, and scenario. Analysis of the dataset reveals that, through the lens of error detection, using AUs as input into error management affords flexibility to the system and has the potential to improve error detection response rate. In addition, we provide an example real-time interactive robot error management system using the error-aware framework.
Bio: Maia Stiber is a 4th year Ph.D. candidate in the Department of Computer Science, co-advised by Dr. Chien-Ming Huang and Dr. Russell Taylor. She received a B.S. in Computer Science from Caltech in 2019 and a M.S.E. in Computer Science from Johns Hopkins University in 2021. Her work focuses on leveraging natural human responses to robot errors in an effort to develop flexible error management techniques in support of effective human-robot interaction.
Victor Antony “Co-designing with older adults, for older adults: robots to promote physical activity.”
Abstract: Lack of physical activity has severe negative health consequences for older adults and limits their ability to live independently. Robots have been proposed to help engage older adults in physical activity (PA), albeit with limited success. There is a lack of robust understanding of older adults’ needs and wants from robots designed to engage them in PA. In this paper, we report on the findings of a co-design process where older adults, physical therapy experts, and engineers designed robots to promote PA in older adults. We found a variety of motivators for and barriers against PA in older adults; we, then, conceptualized a broad spectrum of possible robotic support and found that robots can play various roles to help older adults engage in PA. This exploratory study elucidated several overarching themes and emphasized the need for personalization and adaptability. This work highlights key design features that researchers and engineers should consider when developing robots to engage older adults in PA, and underscores the importance of involving various stakeholders in the design and development of assistive robots.
Bio: Victor Antony is a second-year Ph.D. student in the Department of Computer Science, advised by Dr. Chien-Ming Huang. He received the Bachelor of Science degree in Computer Science from the University of Rochester in 2021. His work focuses on Social Robots for well-being.