Calendar

Nov
16
Wed
LCSR Seminar: Student Seminar @ Hackerman B17
Nov 16 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2022/2023 school year

 

Student 1: Maia Stiber “Supporting Effective HRI via Flexible Robot Error Management Using Natural Human Responses”

Abstract: Unexpected robot errors during human-robot interaction are inescapable; they can occur during any task and do not necessarily fit human expectations of possible errors. When left unmanaged, robot errors’ impact on an interaction harms task performance and user trust, resulting in user unwillingness to work with a robot. Prior error management techniques often do not possess the versatility to appropriately address robot errors across tasks and error types as they frequently use task or error specific information for robust management. In this presentation, I describe my work on exploring techniques for creating flexible error management through leveraging natural human responses (social signals) to robot errors as input for error detection and classification across tasks, scenarios, and error types in physical human-robot interaction. I present an error detection method that uses facial reactions for real-time detection and temporal localization of robot error during HRI,  a flexible error-aware framework using traditional and social signal inputs that allow for improved error detection, and an exploration on the effects of robot error severity on natural human responses. I will end my talk by discussing how my current and future work further investigates the use of social signals in the context of HRI for flexible error detection and classification.

Bio: Maia Stiber is a Ph.D. candidate in the Department of Computer Science, co-advised by Dr. Chien-Ming Huang and Dr. Russell Taylor. 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.

 

 

Student 2: Akwasi Akwaboah “Neuromorphic Cognition and Neural Interfaces”

Abstract: I present research at the Ralph Etienne-Cummings-led Computational Sensor-Motor Systems Lab, Johns Hopkins University on two fronts – (1) Neuromorphic Cognition (NC) focused on the emulation neural physiology at algorithmic and hardware levels, and (2) Neural Interfaces with emphasis on electronics for neural MicroElectrode Array (MEA) characterization. The motivation for the NC front is as follows. The human brain expends a mere 20 watts in learning and inference, exponentially lower than state-of-the-art large language models (GPT-3 and LaMDA). There is the need to innovate sustainable AI hardware as the 3.4x compute doubling per month has drastically outpaced Moore’s law, i.e., a 2-year transistor doubling. Efforts here are geared towards realizing biologically plausible learning rules such as the Hebb’s rule-based Spike-Timing-Dependent Plasticity (STDP) algorithmically and in correspondingly low-power mixed analog-digital VLSI implements. On the same front of achieving a parsimonious artificial intelligence, we are investigating the outcomes of using our models of the primate visual attention to selectively sparsify computation in deep neural networks. At the NI front, we are developing an open-source multichannel potentiostat with parallel data acquisition capability. This work holds implications for rapid characterization and monitoring of neural MEAs often adopted in neural rehabilitation and in neuroscientific experiments. A standard characterization technique is the Electrochemical Impedance (EI) Spectrometry. However, the increasing channel counts in state-of-the-art MEAs (100x and 1000x) imposes the curse of prolonged acquisition time needed for high spectral resolution. Thus, a truly parallel EI spectrometer made available to the scientific community will ameliorate prolonged research time and cost.

Bio: Akwasi Akwaboah joined the Computational Sensory-Motor Systems (CSMS) Lab in Fall 2020 and is working towards his PhD. He received the MSE in Electrical Engineering from the Johns Hopkins University, Baltimore, MD in Summer 2022 en route the PhD. He received the B.Sc. Degree in Biomedical Engineering (First Class Honors) from the Kwame Nkrumah University of Science and Technology, Ghana in 2017. He also received the M.S. degree in Electronics Engineering from Norfolk State University, Norfolk, VA, USA in 2020. His master’s thesis there focused on the formulation of a heuristically optimized computational model of a stem cell-derived cardiomyocyte with implications in cardiac safety pharmacology. He subsequently worked at Dr. James Weiland’s BioElectronic Vision Lab at the University of Michigan, Ann Arbor, MI, USA in 2020; where he collaborated on research in retinal prostheses, calcium imaging and neural electrode characterization. His current interests include the following: neuromorphic circuits and systems, bio-inspired algorithms, computational biology, and neural interfaces. On the lighter side, Akwasi loves to cook and listen to classical and Afrobeats music. He lives by Marie Curie’s quote – “Nothing in life is to be feared, it is only to be understood …

 

Nov
30
Wed
LCSR Seminar: Careers in Robotics: A Panel Discussion With Experts From Industry and Academia @ Hackerman B17
Nov 30 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2022/2023 school year

 

Panel Speaker 1: Erin Sutton, PhD

Guidance and Control Engineer at the JHU Applied Physics Laboratory

Ph.D. Mechanical Engineering 2017, M.S. Mechanical Engineering 2016

Erin Sutton is a mechanical engineer at Johns Hopkins Applied Physics Laboratory. She received a BS in mechanical engineering from the University of Dayton and an MS and a PhD in mechanical engineering from Johns Hopkins University. She spent a year at the Naval Air Systems Command designing flight simulators before joining APL in 2019. Her primary research interest is in enhancing existing guidance and control systems with autonomy, and her recent projects range from hypersonic missile defense to civil space exploration.

 

Panel Speaker 2: Star Kim, PhD

Job title and affiliation: Management Consultant at McKinsey & Company

Ph.D. Mechanical Engineering 2021

Star is an Associate at a global business management consulting firm, McKinsey & Company. At JHU, she worked on personalizing cardiac surgery by creating patient specific vascular conduits at Dr. Axel Krieger’s IMERSE lab. She made a virtual reality software for doctors to design and evaluate conduits for each patient. Her team filed a patent and founded a startup together, which received funding from the State of Maryland. Before joining JHU, she was at the University of Maryland, College Park and the U.S. Food and Drug Administration. There, she developed and tested patient specific medical devices and systems such as virtual reality mental therapy and orthopedic surgical cutting guides.

 

Panel Speaker 3: Nicole Ortega, MSE

Senior Robotics and Controls Engineer at Johnson and Johnson, Robotics and Digital Solutions

JHU MSE Robotics 2018, JHU BS in Biomedical Engineering 2016

At Johnson and Johnson Nicole works on the Robotis and Controls team to improve the accuracy of their laparoscopic surgery platform.  Before joining J&J, Nicole worked as a contractor for NASA supporting Gateway and at Think Surgical supporting their next generation knee arthroplasty robot.

 

Panel Speaker 4: Ryan Keating, MSE

Software Engineer at Nuro

BS Mechanical Engineering 2013, MSE Robotics 2014

Bio: After finishing my degrees at JHU, I spent two and a half years working at Carnegie Robotics, where I was primarily involved in the development of a land-mine sweeping robot and an inertial navigation system. Following a brief stint working at SRI International to prototype a sandwich-making robot system (yes, really), I have been working on the perception team at Nuro for the past four and a half years. I’ve had the opportunity to work on various parts of the perception stack over that time period, but my largest contributions have been to our backup autonomy system, our object tracking system, and the evaluation framework we use to validate changes to the perception system.

Dec
9
Fri
LCSR Winter Potluck/ Ugly Sweater Bash @ Levering Hall - Glass Pavilion
Dec 9 @ 5:00 pm – 7:00 pm

 

 

All LCSR members, their families, and significant others are invited to the:

 

Ugly (or normal) Sweater Bash
Friday, December 9th
5:00PM-7:00PM
Glass Pavilion

 

You can help by contributing your favorite holiday dish (regional specialties strongly encouraged!) to this pot-luck get together (you don’t have to bring anything to participate). Main dishes will be provided, as will plates, napkins, utensils, etc. Click here to sign up

 

There will a gingerbread decorating contest and prizes for best/ugliest sweater!

 

 

 

 

 

 

 

 

 

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

3400 North Charles Street, Baltimore, MD 21218-2608

Laboratory for Computational Sensing + Robotics