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Tyler Schrenk

dblp:341/9296 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0002-9181-0687ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Human-robot interaction · 87% Health and well-being technologies · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
assistive robotics
1.522025
Lessons Learned from Designing and Evaluating a Robot-assisted Feeding System for Out-of-Lab Use · HRI 2025
Design Principles for Robot-Assisted Feeding in Social Contexts · HRI 2023
Human-robot interaction › assistive robotics
robot-assisted feeding
1.522025
Lessons Learned from Designing and Evaluating a Robot-assisted Feeding System for Out-of-Lab Use · HRI 2025
Design Principles for Robot-Assisted Feeding in Social Contexts · HRI 2023

Methods — techniques the papers use, named apart from their topics

user study · 0.9community-based participatory research · 0.9speculative video · 0.7participatory design · 0.7interviews · 0.7
YearPublicationVenuePosition
2025 Lessons Learned from Designing and Evaluating a Robot-assisted Feeding System for Out-of-Lab Use
abstract
Millions of people cannot eat independently due to a disability, and caregiver-assisted meals can make them feel self-conscious, pressured, or burdensome. Robot-assisted feeding promises to empower people with motor impairments to feed themselves. However, current research typically examines specific robotic system subcomponents and evaluates them in controlled lab settings. This leaves a gap in developing and evaluating an end-to-end system that can feed entire meals in out-of-lab settings. We present one such system, which we developed collaboratively with two community researchers (CRs) with motor-impairments. The key challenge of developing a robot feeding system for out-of-lab use is the varied off-nominal scenarios that inevitably arise. Our key insight is that users can overcome many off-nominals, provided customizability and control over the system. Our system improves upon the state-of-the-art with: (1) a user interface that provides substantial user customizability and control, (2) a bite selection implementation that incorporates users-in-the-loop to generalize across food items, and (3) portable hardware that facilitates system use in diverse environments without inhibiting user mobility. We conduct two studies to evaluate the system. In Study 1, five users with motor impairments and one CR use the system to feed themselves meals of their choice in a cafeteria, office, or conference room. In Study 2, one CR uses the system in his home for five days, feeding himself 10 meals across diverse contexts. We present 3 key lesson learned: (1) spatial contexts are numerous, customizability lets users adapt to them; (2) off-nominals will arise, variable autonomy lets users overcome them; and (3) assistive robots' benefits depend on context. We provide video footage and code on our website.
Amal Nanavati, Ethan K. Gordon, Taylor Kessler Faulkner, Yuxin Ray Song, Jonathan Ko, Tyler Schrenk, Vy Nguyen, Hao Zhu 0008, Haya Bolotski, Atharva Kashyap, Sriram Kutty, Raida Karim, Liander Rainbolt, Rosario Scalise, Hanjun Song, Ramon Qu, Maya Cakmak, Siddhartha S. Srinivasa
HRI6
2023 Design Principles for Robot-Assisted Feeding in Social Contexts
abstract
Social dining, i.e., eating with/in company, is replete with meaning and cultural significance. Unfortunately, for the 1.8 million Americans with motor impairments who cannot eat without assistance, challenges restrict them from enjoying this pleasant social ritual. In this work, we identify the needs of participants with motor impairments during social dining and how robot-assisted feeding can address them. Using speculative videos that show robot behaviors within a social dining context, we interviewed participants to understand their preferences. Following a community-based participatory research method, we worked with a community researcher with motor impairments throughout this study. We contribute (a) insights into how a robot can help overcome challenges in social dining, (b) design principles for creating robot-assisted feeding systems, (c) and an implementation guide for future research in this area. Our key finding is that robots' unique assistive qualities can address challenges people with motor impairments face during social dining, promoting empowerment and belonging.
Amal Nanavati, Patrícia Alves-Oliveira, Tyler Schrenk, Ethan K. Gordon, Maya Cakmak, Siddhartha S. Srinivasa
HRI3