Tianyi Gu

Pic of Tianyi Gu, taken Feb 2019
Email of Tianyi Gu

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


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).


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.


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.


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]

  • 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]

  • 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]

  • 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]


  • 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.


My first name is pronounced like read three English letters `T-N-E'.

Some photographs I've taken are posted here.