Ting-Han Lin

CS PhD student at UChicago

University of Chicago

Human-Robot Interaction Lab

Actuated Experience Lab

Biography

Hello! My name is Ting-Han Lin, and I go by Timmy. I am a computer science PhD student advised by Prof. Sarah Sebo at the University of Chicago. I received both M.S. in Computer Science and B.A. in Economics from the University of Chicago in 2022 (4-year Joint BA/MS in Computer Science). During my master’s and undergrad, I was advised by Prof. Sarah Sebo and Prof. Ken Nakagaki.

My research focuses on designing socially intelligent robots that build long-term relationships with people. I develop robots that navigate group dynamics, build rapport through communication, and adapt to users’ needs over time. Through studies in real-life settings with adults and children, I investigate how these capabilities support sustained engagement and trustworthy human–robot interactions. I have published my research at leading conferences, including HRI and CHI.

Research interests: Long-Term Human-Robot Interaction · Human-Robot Rapport · Child-Robot Interaction · In-Home Robot Deployment · Scale Development

Publication & Journals

[HRI 2026 Pioneers] Understanding the Formation of Human-Robot Rapport to Facilitate Positive Long-Term Human-Robot Interaction

We discuss our prior, current, and future works that operationalize human-robot rapport, understand factors that affect rapport across sessions, and test how robot behaviors can sustain rapport in the long term. In our prior work, we developed the Connection-Coordination Rapport (CCR) Scale to measure human-robot rapport. In our current work, we use this scale to investigate how a robot’s social behaviors (empathy, self-disclosure) influence rapport in a three-session study. Finally, our future work will explore how a robot leverages prior conversations with the user to sustain rapport over three weeks.

Ting-Han Lin

[HRI 2026 Pioneers] Understanding the Formation of Human-Robot Rapport to Facilitate Positive Long-Term Human-Robot Interaction

[THRI 2026] The Reduced-Length Connection-Coordination Rapport (CCR) Scale

In this work, we developed a reduced-length version of the CCR scale that still effectively measures rapport using just 8 items. We validated the reduced-length CCR scale by showing that it has high internal reliability, high overlap with the full-length CCR scale, a consistent factor structure, high construct validity, and significant time savings.

Ting-Han Lin, Guan Chen, Bilge Mutlu, J. Gregory Trafton, Sarah Sebo

[THRI 2026] The Reduced-Length Connection-Coordination Rapport (CCR) Scale

[CHB 2025] The Impact of a Robot's Agreement (or Disagreement) on Human-Human Interpersonal Closeness in a Two-Person Decision-Making Task

Our results demonstrate that interacting with the High Anthropomorphism Robot led to greater interpersonal closeness between participants when the robot provided positive feedback as opposed to negative feedback. The Low Anthropomorphism Robot had a different and opposite effect, interactions with this robot led to greater interpersonal closeness when the robot’s feedback was equal as opposed to unequal and when the robot provided negative feedback as opposed to positive feedback.

Ting-Han Lin, Yuval Rubin Kopelman, Madeline Busse, Sarah Sebo*, Hadas Erel* (*equal contribution)

[CHB 2025] The Impact of a Robot's Agreement (or Disagreement) on Human-Human Interpersonal Closeness in a Two-Person Decision-Making Task

[HRI 2025] Connection-Coordination Rapport (CCR) Scale: A Dual-Factor Scale to Measure Human-Robot Rapport

Results from these studies demonstrate high reliability and validity for the CCR scale, which can be used to measure rapport in both first-person and third-person perspectives. We encourage the adoption of this scale in future studies to measure rapport in a variety of human-robot interactions.

Ting-Han Lin, Hannah Dinner, Tsz Long Leung, Bilge Mutlu, J. Gregory Trafton, Sarah Sebo

[HRI 2025] Connection-Coordination Rapport (CCR) Scale: A Dual-Factor Scale to Measure Human-Robot Rapport

[HRI 2024] Role-Playing with Robot Characters: Increasing User Engagement through Narrative and Gameplay Agency

Our results show that increasing user agency in a role-playing experience, in either its narrative or its gameplay, improves users' flow state, sense of autonomy and competence, verbal engagement, and perceptions of the robot characters' engagement. Increasing narrative agency also led to longer unprompted reactions from participants, while gameplay agency improved feelings of immersion and relatedness with the robots. These findings suggest that creating either narrative or gameplay agency can improve user engagement, which can extend to broader robot interactions where gameplay elements and role-playing in stories can be incorporated.

Spencer Ng, Ting-Han Lin, You Li, Sarah Sebo

[HRI 2024] Role-Playing with Robot Characters: Increasing User Engagement through Narrative and Gameplay Agency

[CHI 2023] ThrowIO: Actuated TUIs that Facilitate “Throwing and Catching” Spatial Interaction with Overhanging Mobile Wheeled Robots

By computationally facilitating throwing and catching interaction, our approach can be applied in many applications including kinesthetic learning, gaming, immersive haptic experience, ceiling storage, and communication. We demonstrate the applications with a proof-of-concept system enabled by wheeled robots, ceiling hardware design, and software control. Overall, ThrowIO opens up novel spatial, dynamic, and tangible interaction for users via overhanging robots, which has great potential to be integrated into our everyday space.

Ting-Han Lin, Willa Yunqi Yang, Ken Nakagaki

[CHI 2023] ThrowIO: Actuated TUIs that Facilitate “Throwing and Catching” Spatial Interaction with Overhanging Mobile Wheeled Robots

[CHI 2023] Ice-Breaking Technology: Robots and Computers Can Foster Meaningful Connections between Strangers through In-Person Conversations

We found that the humanoid robot facilitator was able to elicit the greatest compliance with the deep conversation questions. Additionally, participants in conversations facilitated by either the humanoid robot or the computer screen reported greater happiness and connection to their conversation partner than those in conversations facilitated by a poster. These results suggest that technology-enabled conversation facilitators can be useful in breaking the ice between strangers.

Alex Wuqi Zhang, Ting-Han Lin, Xuan Zhao, Sarah Sebo

[CHI 2023] Ice-Breaking Technology: Robots and Computers Can Foster Meaningful Connections between Strangers through In-Person Conversations

[RO-MAN 2022] Benefits of an Interactive Robot Character in Immersive Puzzle Games

Our results show that participants playing with a robot had more fun, felt less judged, and felt more connected with the robot while solving tasks compared to those playing with a human. These results suggest that robots can be effective alternatives to human actors in broader immersive entertainment contexts such as escape rooms to provide greater enjoyment and promote more social interaction with in-game characters.

Ting-Han Lin*, Spencer Ng*, Sarah Sebo (*equal contribution)

[RO-MAN 2022] Benefits of an Interactive Robot Character in Immersive Puzzle Games

[IFJ 2019] Autonomous Cars: Driving the Future Forward?

No longer just a relic of science fiction movies, autonomous cars have become a reality. Autonomous cars are heavily researched and developed by both large technology companies and traditional car manufacturers.Within a couple of years, autonomous cars will be able to transport people from one place to another on a massive scale. However, their emergence would actually lead to safety concerns and umemployment crisis. With these drawbacks in mind, would autonomous cars drive our future forward?

Ting-Han Lin

[IFJ 2019] Autonomous Cars: Driving the Future Forward?

News

09/18/2026 Submitted two first-author papers to HRI 2027!

03/22/2026 Presented at HRI 2026 Pioneers in Edinburgh, Scotland!

01/13/2026 First-author paper accepted to Transactions on Human-Robot Interaction!

Projects & Fun Stuff

Korean Pronunciation Practice

A fast, lightweight game for practicing Korean pronunciation through Hangul recall. Players choose focused practice sets for the alphabet, basic consonants, double consonants, simple vowels, or compound vowels, type the matching romanized pronunciation, receive immediate feedback, and review results at the end of each round.

Timmy in Crerar

Timmy in Crerar is a lightweight presence-tracking project: a Chrome extension running in Timmy’s school Chrome profile checks whether his laptop is active on eduroam / UChicago network conditions associated with Crerar, a Supabase backend stores clean heartbeat and session data, and this website publishes a read-only public dashboard.

Sneaky EV Charger Dashboard

A sneaky little EV charger dashboard hosted on Render that lets you peek at real-time availability at secret Chicago spots (shh…ask Timmy). No more checking chargers in-person or clicking through boring links.

Pinyin Typing Trainer

A fast, lightweight game for practicing Mandarin pinyin through timed character recall. Players type the correct pinyin for randomly selected Traditional Chinese characters, receive immediate feedback, and review mistakes with correct answers at the end of each round.

NAO Python3 Demo

We can finally control NAO robot in Python3! This repository contains the nao_demo.py script, demonstrating a simplified NAO interaction with a user from Experiment 1 in The Impact of a Robot’s Agreement (or Disagreement) on Human-Human Interpersonal Closeness in a Two-Person Decision-Making Task.

Between the Words

With the popularity of digital games, social media, and other data-sharing platforms, more and more individuals' information is tracked and used without users being aware. Often, people give companies information about themselves without knowing how that data may be shared or sold to other parties.

Between the Words