Call for Participation
Talking Robotics
A series of talks promoting reflections and dialogue on robotics and its interaction with other relevant research fields
https://talking-robotics.github.io/
First Zoom session: September 4, 2020
We acknowledged the broader interest of robotics students and researchers on diverse aspects related to working with robots and we aim to accommodate different discussions that can sparkle knowledge in different directions in the field of robotics. In our readings we aim to include material/research/papers/discussions surrounding automation, algorithms, user studies, and systems.
FORMAT
We are having virtual meetings on Zoom. The sessions are once every two weeks and are in the form of round-table discussions where everyone is encouraged to contribute and share ideas. The discussion will be guided and moderated by us, to facilitate the flow of ideas and ensure we get somewhere by the end of each session. Readings, recordings and meeting minutes will be posted online for your reference or in case you have to miss one of the sessions.
SCHEDULE
We host virtual sessions bi-weekly, i.e., every other week. We allocate 30min for presentation time and 30min for discussion and networking. Our first session will be on Sept 4, Friday at 9am PDT. Details are below.
Check our calendar or download the .ics
TALKING ROBOTICS #1
SAVE THE DATE
September 4, Friday, 9am Pacific Time
Zoom link: https://washington.zoom.us/j/95943842399
Meeting ID: 959 4384 2399
*The talk and discussion will be recorded and shared publicly.
SPEAKER
Michael Jae-Yoon Chung is a graduate student at the University of Washington whose research focus is on end-user programming for authoring interactive robot behaviors.
TITLE AND ABSTRACT
Iterative Repair of Social Robot Programs from Implicit User Feedback via Bayesian Inference
By Michael Jae-Yoon Chung and Maya Cakmak
Robotics: Science and Systems 2020. Corvalis, Oregon, USA, July 12-16, 2020
Paper link
Creating natural and autonomous interactions with social robots requires rich, multi-modal sensory input from the user. Writing interactive robot programs that make use of this input can demand tedious and error-prone tuning of program parameters, such as tuning thresholds on noisy sensory streams for detecting whether the robot’s user is engaged or not. This tuning process dealing with low-level streams and parameters makes programming of social robots time-consuming and inaccessible for people who could benefit the most from unique use cases of social robots. To address this challenge, we propose the use of iterative program repair, where programmers create an initial program sketch in our new Social Robot Program Transition Sketch Language (SoRTSketch), a domain-specific language that supports expressing uncertainties related to thresholds in transition functions. The program is then iteratively repaired using Bayesian inference based on corrections of interaction traces that are either provided by the programmer or derived from implicit feedback given by the user during the interaction. Based on experiments with a human simulator and with 10 human users, we demonstrate the ease and effectiveness of this approach in improving social robot programming and program outputs that represent three common human-robot interaction patterns. We also show how our approach helps programs adapt to environment changes over time.
TEASER VIDEO
https://youtu.be/LbcMxC_3x1U
TALKING ROBOTICS ORGANIZERS
Patrícia Alves Oliveira, University of Washington (USA)
Silvia Tulli, Instituto Superior Técnico (Portugal) and Soborne University (France)
Miguel Vasco, Instituto Superior Técnico (Portugal)
Joana Campos, Instituto Superior Técnico (Portugal)
CONTACT
Drop us a Hi – talkingrobotics[at]gmail[dot]com or Follow us on YouTube, Twitter