Tianyi Gu
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I am a Ph.D. candidate at the University of New
Hampshire in the computer science department. I am
a member of the UNH Artificial Intelligence
Group. I am currently working with Professor
Wheeler Ruml in the area of
Artificial Intelligence, Heuristic Search,
Robotics and Motion Planning.
Here are my full CV,
GitHub page,
Google Scholar citations,
and LinkedIn profile
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Experience
University of New Hampshire
I have been a teaching assistant at the
University of New Hampshire for a variety of
courses, involved in creating assignments, exams and
conducting recitation sessions for Algorithms (C), Intro
to
AI, Intro to Computer Science (Java, Python), Intro to
Software Engineering (DevOps tools), Intro to Computer
Security, Database Programming (C#, SQL), Scripting
Languages (Shell).
Motional
I was a research intern in the planning
team at Motional 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
for autonomous vehicles.
Cognitive Assistive Robotics Lab
I was a robotics intern at CARL
in the
summer of 2019. I was working on a research project of
socially assistive robot mediated smart home intervention
to support caregiving of individuals with Alzheimer's
disease at
home. In the project, I
-
Build a smart home based service robot framework that
can provide real-time Alzheimer's disease care.
-
Build a AI planner based on ROSPlan that performs
real-time online task planning.
All of the source codes can be found here.
Here
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.
Here
is my talk about this project and
slides
In this video,
I demo the system to a group of undergrads.
Realtime Robotics
I was a robotics intern at Realtime Robotics in the
summer of 2018. I was working 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 is able to safely drive in a crowded urban area.
The planner was lattice-based and performed an anytime
search.
-
Build a real-time planning framework that enable
handling a dynamic world online.
-
Build a simulation environment to demonstrate flagship
product to major new customers.
Here
is a video shows the vehicle avoid hitting a man who
rushed into the road. Here
is another video that shows that the vehicle obeys the
traffic
light. The online lattice is also visualized in this
video. Here
is my talk about this project and slides.
SIPG
I worked as a operations researcher and a software
engineer at the Shanghai International Port Group
for 3 years (2012-2015) after I graduated from the Shanghai
Maritime University with my Master Degree in
Logistics Engineering. While I was at the SIPG I worked in
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
module.
- Helped launch previous terminal operating management
system.
Alcatel-Lucent
During the summer of 2011 I worked as an software
engineer intern at Alcatel-Lucent (Shanghai). While
there, I developed a global electrical elements database,
including web interface and database maintenance software.
Peer-reviewed Publications
-
Maximilian Fickert, Tianyi Gu, Leonhard Staut,
Sai Lekyang,
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
(PRL-20), 2020.
[pdf]
[publisher]
[slides]
[poster]
[talk]
[code]
-
Tianyi Gu, Momotaz Begum, Naiqian Zhang, Dongpeng Xu, Sajay Arthanat, and Dain
P. LaRoche, An Adaptive Software Framework for
Dementia-care Robots.
Proceedings of the ICAPS Workshop on Planning and
Robotics (PlanRob-20), 2020.
[pdf]
[publisher]
[slides]
[video]
[talk]
[code]
-
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
(AAAI-20), 2020.
[pdf]
[publisher]
[slides]
[poster]
[code]
-
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.
[pdf]
[publisher]
-
Bence Cserna, Wiliam J. Doyle, Tianyi Gu, and
Wheeler Ruml,
Safe Temporal Planning for Urban Driving,
Proceedings of the AAAI Workshop on
Artificial Intelligence Safety (SafeAI-19), 2019.
[pdf]
[publisher]
[slides]
[poster]
-
Reazul H. Russel, Tianyi Gu, and Marek Petrik,
Robust Exploration with Tight Bayesian Plausibility
Sets,
Proceedings of the 4th Multidisciplinary
Conference on Reinforcement Learning and
Decision Making (RLDM), 2019.
[pdf]
[poster]
-
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.
[pdf]
[publisher]
[video] [talk]
[slides]
[code]
-
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.
[pdf] [publisher]
-
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.
[pdf] [publisher]
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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.
[pdf] [publisher]
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Chengji Liang, Tianyi Gu, Bo Lu, and Yi Ding,
Genetic Mechanism-based Coupling Algorithm for Solving
Coordinated Scheduling Problems of Yard Systems in
Container Terminals ,
Computers & Industrial Engineering 89 34–42,
2015.
[pdf] [publisher]
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Yi Ding, Tianyi Gu, Guolong Lin, and Chengji
Liang,
The Establishment and Solution of Coupling Model on
Coordinated Scheduling of Handling Facilities in
Container Terminals ,
Applied Mathematics & Information Sciences 6(3)
915–924, 2012.
[pdf] [publisher]
Research Visits and Invited Talks
-
2018 Guest lecture for the University
of New Hampshire's CS900: Graduate Seminar.
[talk]
[slides]
-
2018 Invited lecture at the University
of New Hampshire's Robotics Seminar Series.
[talk]
[slides]
-
2018 Invited lecture at Shanghai
Maritime University's Logistics Research
Center.
[talk]
[slides]
-
2017 Invited lecture at the University
of New Hampshire's Robotics Seminar Series.
[talk]
[slides]
Code
-
In CS880 Introduction to Mobile Robotics, my final
project study 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 autonomously
build a map for an unknown environment. The theory of
Bayesian inference has been applied to update an
occupancy map and the frontier based exploration
algorithm has been applied to navigate robot to unknown
areas in the map. The experiment results show that the
robot is able to map the environment with fully
autonomous both in simulation and real world
environments. All of the source codes can be found here.
We also took a video that record the process of a
Turtlebot mapping a real world environment.
- In CS980 Topics in Reinforcement Learning,
my final
project study the problem of dynamic
obstacles avoidance for mobile
robots. We studied two deterministic
approaches which use heuristic search
techniques, and four stochastic
approaches which use reinforcement
learning techniques. We proved these two
type of approach are mathematically
different. The experiment results show
that deterministic approaches are not
only faster but also robuster than
stochastic approaches. But stochastic
approaches still applicable for certain
problem scenarios. All of the source codes can be
found here.
- In CS980 Planning for Robots,
my final
project design two control algorithm: sampling
based model-predictive control (SBMPC) and
bisection search based model-predictive control
(BBMPC). The algorithms are implemented as the
controller for a real-time planning system in ROS
to enable a Pioneer robot to move quickly in
environments with dynamic obstacles. The behaviors
of both algorithms are demonstrated through
straight and curve line following experiments from
simulation and real world environments. We also
discussed several issues of the real-time
planning system. All of the source codes can be
found HERE.
- In CS830 Introduction to Artificial
Intelligence, my final project present a new
anytime motion planning approach called B-SST. B-SST
first runs BEAST, an effort-aided planner, to find a
first solution as quickly as possible, then switch to
another motion tree growth process called
SST-with-cost-pruning, which adopt both idea from SST
and cost pruning algorithms. We first introduce several
related work that we build upon. Then B-SST is described
in detail. Results with a variety of 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 on how to
create a better anytime motion planner in the end. All
of the source codes can be found here.
- In CS880 Introduction to Information Retrieval,
my final project study the task of
helping an automated player win a computer game by
reading a strategic user's guide designed for human
players. In complex computer games such as Star Craft,
War Craft, 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 using such textual information
to train an automated playe. Our goal, is to better
understand the retrieval model used in their paper in
terms of algorithms and metrics from the field of
information retrieval. Our results provide some evidence
that inverse document frequency out performs recurrent
neural networks at assisting human players, and could be
used as a baseline for evaluating retrieval models used
in playing games like this. All of the source codes can
be found here.
- In CS880 Probabilistic AI and Machine
Learning,
my final project compared the
accuracy and reliability of several different
classifiers for recognizing handwritten digit,
training on MNIST dataset. Classifiers such as
K-Nearest Neighbors, Decision Tree, and Random
Forest are applied to the problem, and it turns out
they both have their pros and cons. All of the source
codes can be found here.
- In CS980 Topics in Multi-Agent and Multi-Robot
Systems, my final project was an MDP based
approach to slove the Quay Crane Scheduling Problem
under Uncertainty in Container Terminals. The number of
time-conflict tasks for yard cranes (YC) in yard
operation is considered as one of the important measures
for the evaluation of the level of fluency for quay
crane scheduling by terminal experts. In this project,
an MDP model for QCSP is developed. A UCT algorithm is designed to solve
the model.
Personal
My first name is pronounced like read three English
letters `T-N-E'.
Some photographs I've taken are posted here.
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