Alexis E. Block received her bachelors degree in Mechanical Engineering and Applied Mechanics and two minors in Math and Engineering Entrepreneurship in 2016 from the University of Pennsylvania. The following year, 2017, she received her masters degree in Robotics, also from the University of Pennsylvania.
Alexis E. Block's doctoral research, HuggieBot, focuses on social-physical human-robot interaction. She currently works with her primary doctoral advisor, Dr. Katherine J. Kuchenbecker, in the Haptic Intelligence Department of the Max Planck Institute for Intelligent Systems as a Center for Learning Systems (CLS) Doctoral Fellow. She is affiliated with the Computer Science Department at ETH Zürich. Her two ETH co-advisors are Roger Gassert (Rehabilitation Engineering Laboratory) and Otmar Hilliges (Advanced Interactive Technologies).
Block was named a 2018 HRI Pioneer, and elected a General Chair for HRI Pioneers 2019.
Alexis Block's research has been featured on several radio programs:
16 June 2018, NPR: HuggieBot was the first question discussed during the Panel Questions segment of NPR's "Wait Wait Don't Tell Me" program.
15 June 2018, Paul Ross Show on TalkRadio: Alexis had an 11 minute interview with Paul Ross. It aired between 4:30-5:00 and begins about 8 minutes in.
Block's research has been featured in several news articles:
Workshop paper (2 pages) oral presentation and poster presented at the HRI Pioneers Workshop, March 2018 (misc)
Hugs are one of the first forms of contact and affection humans experience. Due to their prevalence and health benefits, we want to enable robots to safely hug humans. This research strives to create and study a high fidelity robotic system that provides emotional support to people through hugs. This paper outlines our previous work evaluating human responses to a prototype’s physical and behavioral characteristics, and then it lays out our ongoing and future work.
Workshop Paper (2 pages) presented at the RO-MAN Workshop on Social Interaction and Multimodal Expression for Socially Intelligent Robots, August 2017 (misc)
A hug is one of the most basic ways humans can express affection. As hugs are so common, a natural progression of robot development is to have robots one day hug humans as seamlessly as these intimate human-human interactions occur. This project’s purpose is to evaluate human responses to different robot physical characteristics and hugging behaviors. Specifically, we aim to test the hypothesis that a warm, soft, touch-sensitive PR2 humanoid robot can provide humans with satisfying hugs by matching both their hugging pressure and their hugging duration. Thirty participants experienced and evaluated twelve hugs with the robot, divided into three randomly ordered trials that focused on physical robot char- acteristics and nine randomly ordered trials with varied hug pressure and duration. We found that people prefer soft, warm hugs over hard, cold hugs. Furthermore, users prefer hugs that physically squeeze them and release immediately when they are ready for the hug to end.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems