University of New Hampshire RA, TA | 6
I have been a research assistant at the UNH AI Group. I
conduct theoretical and empirical research
projects to design principled algorithms for
autonomous agents to plan in an uncertain
environment under time pressure. My works
have been published in several AI conferences such as
AAAI, IJCAI, ICAPS, and IROS. Please see the publication
session below for more details.
I have also been a teaching assistant at the
University of New Hampshire for a variety of
courses, involved in creating assignments and exams and
conducting recitation sessions for Algorithms (C), Intro
AI, Intro to Computer Science (Java, Python), Intro to
Software Engineering (DevOps tools), Intro to Computer
Security, Database Programming (C#, SQL), Scripting
Languages (bash, zsh). Here are some teaching samples.
Motional Summer Research
Intern | 12 weeks
I was a research intern in the planning
team at Motional (Aptiv's self-driving team) in
the summer of 2020. I proposed and
implemented a learning-based approach
to enhance the planner. The
feature was integrated into the next-generation planner.
According to the non-disclosure agreement, I
can talk more details only after our in-progress patent is
Cognitive Assistive Robotics Lab Summer
| 12 weeks
I was a robotics intern at CARL
at UNH in the
summer of 2019. I worked on a proof-of-concept research
project that builds a socially assistive robot to support
the caregiving of individuals with Alzheimer's disease. In
the project, I
Build a smart-home-based service robot framework that
can provide real-time Alzheimer's disease care.
Build an AI planner based on ROSPlan that performs
real-time online task planning.
Two papers are published from this project: a robotics
and a gerontology
is my talk at ICAPS and
is a video that shows two caregiving scenarios:
1) the robot reminds the patient to take medication,
2) the robot is preventing a dangerous walkaway of the
patient. The second part of the video record a
group of real-world caregivers participating in our user
I also demoed
the system to a group of
All of the source codes are here
Summer Research Intern | 12 weeks
I was a robotics intern at Realtime Robotics in the
summer of 2018. I worked on a motion planning project
that could enable an autonomous vehicle to safely drive in
crowded urban areas and also achieve the goal regions as
quickly as possible. In the project, I
Build a real-time planner for an autonomous vehicle
that can safely drive in a crowded urban area.
The planner was lattice-based and performed an anytime
Build a real-time planning framework that enables
handling a dynamic world online.
Build a simulation environment to demonstrate flagship
products to significant new customers.
is a video shows the vehicle avoids hitting a man who
rushed into the road. Here
is another video that shows that the car obeys the
light. It also visualizes the online lattice is also.
is my talk about this project and slides
Port of Shanghai
Engineer | 3 years
I worked as an operations research engineer and a
engineer at the Shanghai International Port Group
(SIPG, 上港集团) for three years (2012-2015) after I graduated
from the Shanghai
Maritime University with my Master's Degree in
Logistics Engineering. While I was at the SIPG, I worked
the following projects:
- Member of the team that designed, built and deployed
a new automated container terminal operations management
system, including algorithm development for the crane
allocation and scheduling module and the financial
- Helped launch previous terminal operating management
Engineer Intern | 6 months
I worked as a software engineer intern at Alcatel-Lucent (Shanghai)(上海贝尔) during the
summer of 2011. While
there, I developed a global electrical elements database,
including web interfaces and database maintenance
Maximilian Fickert*, Tianyi Gu*, and Wheeler Ruml,
Bounded-Cost Search Using Estimates of Uncertainty.
Proceedings of the Thirtieth International Joint
Conference on Artificial Intelligence
Maximilian Fickert*, Tianyi Gu*, Leonhard Staut*,
Wheeler Ruml, Joerg Hoffmann, and Marek Petrik,
Real-time Planning as Data-driven Decision-making.
Proceedings of the ICAPS Workshop on Bridging the
Gap Between AI Planning and Reinforcement Learning
Tianyi Gu, Momotaz Begum, Naiqian Zhang, Dongpeng
Xu, Sajay Arthanat, and Dain
P. LaRoche, An Adaptive Software Framework for
Proceedings of the ICAPS Workshop on Planning and
Robotics (PlanRob-20), 2020.
Maximilian Fickert*, Tianyi Gu*, Leonhard Staut*,
Wheeler Ruml, Joerg Hoffmann, and Marek Petrik, Beliefs
We Can Believe In: Replacing Assumptions with Data in
Real-Time Search. Proceedings of the Thirty-fourth
AAAI Conference on Artificial Intelligence
Sajay Arthanat, Momotaz Begum, Tianyi Gu, Dain
P. LaRoche, Dongpeng Xu, and Naiqian Zhang, Caregiver
Perspectives on A Smart Home-based Socially Assistive
Robot for Individuals with Alzheimer's Disease and
Related Dementia. Disability and Rehabilitation:
Assistive Technology, 2020.
Bence Cserna, William J. Doyle, Tianyi Gu, and
Safe Temporal Planning for Urban Driving,
Proceedings of the AAAI Workshop on
Artificial Intelligence Safety (SafeAI-19), 2019.
Reazul H. Russel, Tianyi Gu, and Marek Petrik,
Robust Exploration with Tight Bayesian Plausibility
Proceedings of the 4th Multidisciplinary
Conference on Reinforcement Learning and
Decision Making (RLDM), 2019.
Scott Kiesel, Tianyi Gu, and Wheeler Ruml,
An Effort Bias for Sampling-based Motion Planning,
Proceedings of the IEEE/RSJ Conference on
Intelligent Robots and Systems (IROS), 2017.
Yi Ding, Xujun Wei, Yang Yang, and Tianyi Gu,
Decision Support-based Automatic Container Sequencing
System Using Heuristic Rules, Cluster
Computing 20(1) 239-252, 2017.
Chengji Liang, Miaomiao Li, Bo Lu, Tianyi Gu,
Jungbok Jo, and Yi Ding, Dynamic Configuration of QC
Allocating Problem Based on Multi-objective Genetic
Algorithm, Journal of Intelligent
Manufacturing 28(3) 847-855, 2017.
Yuping Wang, Yangyang Hao, Yuanhui Zhang, Youfang Huang,
and Tianyi Gu,
Berth Allocation Optimization with Priority based on
Simulated Annealing Algorithm,
Journal of Engineering Science & Technology
Review 11(1) 74-83, 2017.
Yi Ding, Shuai Jia, Tianyi Gu, and Chung-Lun Li,
SGICT Builds an Optimization-based System for Daily
Berth Planning ,
Interfaces 46(4) 281-296, 2016.
Chengji Liang, Tianyi Gu, Bo Lu, and Yi Ding,
Genetic Mechanism-based Coupling Algorithm for Solving
Coordinated Scheduling Problems of Yard Systems in
Computers & Industrial Engineering 89 34–42,
Yi Ding, Tianyi Gu, Guolong Lin, and Chengji
The Establishment and Solution of Coupling Model on
Coordinated Scheduling of Handling Facilities in
Applied Mathematics & Information Sciences 6(3)
Research Visits and Invited Talks
2021 Invited talk at the IBM Thomas J. Watson Research
2018 Guest lecture for the University
of New Hampshire's CS900: Graduate Seminar.
2018 Invited lecture at the University
of New Hampshire's Robotics Seminar Series.
2018 Invited lecture at Shanghai
Maritime University's Logistics Research
2017 Invited lecture at the University
of New Hampshire's Robotics Seminar Series.
In CS880 Introduction to Mobile Robotics, my final
project studies the problem of autonomous
mapping. We implement the frontier-based exploration
algorithm combined with the occupancy grid mapping
technique that enables a Turtlebot robot to build a map
autonomously for an unknown environment. We applied the
Bayesian inference to update an occupancy map. We also
used the frontier based exploration
algorithm to navigate the robot to unexplored
areas on the map. The experiment results show that the
robot can map the environment fully
autonomously, both in simulation and in real-world
settings. Our source code is here.
We also took a video that records the process of
Turtlebot mapping a corner area in Kingsbury.
- In CS980 Topics in Reinforcement Learning,
project studies the problem of dynamic
obstacles avoidance for mobile
robots. We studied two deterministic
approaches that use heuristic search
techniques and four stochastic
methods that use reinforcement
learning techniques. We proved these two
types of systems are mathematically
different. The experiment results show
that deterministic approaches are not
only faster but also more robust than
stochastic approaches. But stochastic
methods often applicable for specific
problem scenarios. Our source code is here.
- In CS980 Planning for Robots,
project designs two control algorithms: a
model-predictive control (SBMPC) and a bisection
search-based model-predictive control (BBMPC). We build
controllers on top of these algorithms for a real-time
planning system that enables a Pioneer robot to move
quickly in environments with dynamic obstacles. We use
the ROS platform to establish communication between
different modules (i.e., perception, planner,
executive). We run experiments in both simulation
environments and real-world world environments. The
investigation shows that the algorithms have pros and
cons, respectively, depending on the path’s curvature.
We also discussed several issues with the real-time
planning system. Our source code is here.
- In CS830 Introduction to Artificial
Intelligence, my final project presents a new
anytime motion planning approach called B-SST. B-SST
first runs BEAST, an effort-aided planner, to find a
suboptimal solution as quickly as possible. Then it
switches to another motion tree growth process called
SST-with-cost-pruning that adopts both ideas from SST
and cost pruning algorithms. We first introduce several
related works. Then B-SST is described
in detail. Results with various vehicles and
environments showed that B-SST is competitive compared
to A-BEAST and other successful anytime planners. We
also discussed a more sophisticated idea of creating a
better anytime motion planner in the end. Our source
code is here.
- In CS880 Introduction to Information Retrieval,
my final project studies the task of
helping an AI player win a computer game by
reading a strategic user's guide designed for human
players. In complex computer games such as StarCraft,
WarCraft, and Civilization, finding a winning strategy
is challenging even for humans. Therefore, human players
typically rely on manuals and guides. Recently,
researchers have tried to use such textual information
to train an AI player. Our goal is to better
understand the retrieval models used in their paper. Our
results provide evidence
that shows the basic inverse document frequency (IDF)
can, surprisingly, often outperform approaches that rely
neural networks (RNN). Our source code is here.
- In CS880 Probabilistic AI and Machine
my final project compared the
accuracy and reliability of several different
classifiers for recognizing handwritten digits,
training on MNIST dataset. We implement classifiers
such as K-Nearest Neighbors, Decision Tree, and Random
Forest for comparison. Our results suggest they both
have their pros and cons. Our source code is here.
- In CS980 Topics in Multi-Agent and Multi-Robot
Systems, my final project was an MDP-based
approach to solve the Quay Crane Scheduling Problem
(QCSP) under uncertain demand. In container terminals,
the number of time-conflict tasks for yard cranes (YC)
in yard operation is considered one of the critical
measures for evaluating the level of fluency for quay
crane scheduling by terminal experts. We model QCSP in
MDP, design a reward function to minimize the number of
time-conflict tasks, and apply the UCT algorithm to solve the model.
My first name is pronounced like read three English
Some photographs I've taken are posted here.