Tsz-Chiu Au      Chiu

Associate Professor
School of Electrical and Computer Engineering
Ulsan National Institute of Science and Technology

ART Lab @ UNIST: Agents and Robotic Transportation Lab (a.k.a. AI, Robotics, and Transportation Lab) is a research lab dedicated to Artificial Intelligence and Robotics research. Our goal is to scientifically investigate the foundations of intelligent systems for decision making and problem solving, using techniques such as planning, machine learning, automated reasoning, and game theory. Apart from traditional AI topics, our lab also focuses on AI/robot systems in transportation and logistics domains.

These are my CV and my research statement.


Breaking news!

Current Students:
  • Thuy Nguyen Bui, 2019.
        M.S., Computer Science. Research area: intelligent transportation systems and game theory.
  • Jaebak Hwang, 2018.
        M.S., Computer Science. Research area: reinforcement learning and intelligent transportation systems.
  • Dohee Lee, 2015.
        M.S./Ph.D., Computer Science. Research area: robotics.
Former Students:
  • Sangwoo Ha, 2017.
        M.S., Computer Science. Research area: deep learning.
        Position after M.S. degree: Deepnoid, South Korea
  • Dung Nguyen, 2017.
        M.S., Computer Science. Research area: robotics.
        Position after M.S. degree: Deakin University, Australia
  • Ty V. Nguyen, 2014.
        M.S., Computer Science. Research area: autonomous vehicles control and planning.
        Position after M.S. degree: University of Pennsylvania, USA
Lab Members (former and current):
  • Muhammetmyrat Yarmatov, 2020.
       Research area: autonomous drones.
  • Sapar Charyyev, 2019.
       Research area: autonomous drones.
  • Kiyoung Kwon, 2018.
       Research area: virtual/augmented reality and deep learning
  • Kanybek Asanbekov, 2018.
       Research area: machine learning.
  • Damir Kairzhanov, 2018.
       Research area: autonomous vehicle.
  • Uyoung Jeong, 2018.
       Research area: transportation engineering.
  • Eunchul Song, 2017.
       Research area: machine learning.
  • Minhyuk Park, 2016.
       Research area: autonomous drones.
  • Thuy Nguyen Bui, 2015.
       Research area: game theory.
  • Haeryang Kim, 2015.
       Research area: autonomous vehicles and drones.
  • Yisak Park, 2014.
       Research area: computational economics.
  • Giyoung Jeon, 2013.
       Research area: robotics.
  • Olzhas Kaiyrakhmet, 2013.
       Research area: artificial intelligence.
  • Ahmed Mukhtar, 2013.
       Research area: artificial intelligence.
      
Chiu
Group photo taken in summer 2015.
Summer interns (former and current):
  • Carl Martin Krokeide, Norwegian University of Science and Technology (NTNU), 2018.
  • Carlos Pérez Muñoz, University of Málaga, 2018.
  • Minh Duc Tran, Lehigh University, 2018.
  • Hyeok Sung Kwon, Chungbuk National University, 2018.
  • Geunil Song, Kyungpook National University, 2018.
  • Aybek Smagulov, UNIST, 2018.
  • Sanzhar Aubakir, UNIST, 2018.
  • Tomàš Kello, Czech Technical University in Prague, 2017.
  • Sebastian Golos, Cardiff University, 2017.
  • Alibek Taalaibek uulu, UNIST, 2017.
Teaching:
Previous Affiliations:
Research Equipment:
Selected Projects:
  • High-Density Parking for Autonomous Vehicles. Many cities suffer from the shortage of parking spaces. Research in high density parking (HDP) focuses on how to increase the capacity of parking lots by allowing vehicles to block each other but temporarily give way to other vehicles by driving autonomously upon request. We present the design of autonomous parking lots (APLs), which allows the deployment of different parking strategies in different regions in a parking lot. Our APL design guarantees to be gridlock-free: no vehicle can get stuck in a parking lot forever. Our simulation shows that APLs can hold 60% more vehicles given the same amount of space.
  • Fully Autonomous Drones. A fully autonomous drone is an unmanned aerial vehicle that is entirely controlled by its on-board computer and sensors without any human intervention, and they do not rely on any external sensing or computational power. We are interested to build and program a fully autonomous drone that can fly in a cluttered indoor environment to perform some tasks (e.g., delivering an object to a target location) as quickly as possible. Currently, we are studying how to extend the range of delivery drones by machine learning.
  • Mobile Robots for Manufacturing Automation. Recent advances in artificial intelligence offer new opportunities to enhance manufacturing automation. We are interested to design new robotic systems for manufacturing automation and logistics. More specially, we are currently studying how to combine mobile robots with production machinery such that the manufacturing process can overlap with the delivery process. In addition, we devised mobile conveyors that can play an important role in high throughput robotic systems.
  • Motion Planning for Autonomous Driving. Fully autonomous vehicles are technologically feasible with the current generation of hardware, as demonstrated by recent robot car competitions. While the control of these autonomous vehicles is good enough for driving on today's roads, a more precise control of autonomous vehicles can lead to better utilization of road surfaces and reduce traffic congestion. As the first step, I theoretically examined the relationship between the precision of cars' motion control and the throughput of an intersection, and help developing a mixed-reality simulation platform to empirically show that our setpoint scheduler for brake and throttle actuators can reduce the chance that a vehicle stops before intersections.
  • Autonomous Traffic Management. Looking ahead to the time when autonomous cars will be common, we study how to utilize the capacity of autonomous vehicles to make transportation systems much more efficient. Dresner and Stone proposed an intersection control protocol for autonomous vehicle traffic called Autonomous Intersection Management (AIM), which is more efficient than traffic signals and stop signs. In this project, we expand the scope of AIM to traffic management in road networks, and aim to find out the best transportation infrastructure for traffic that consists of a mix of autonomous vehicles and human-controlled vehicles.
  • Learning in Multiagent Systems. To create better agents in multiagent environments, one may want to examine the observed behaviors of existing agents in order to combine their best skills. But the agents being observed may exhibit incompatible behaviors---the agents choose to do different things in some situations---and it is not immediately clear which behavior is better. I proposed a technique to identify the best subset of observed behaviors, in forms of interaction traces, that can be combined together to form a new strategy called composite strategy that potentially outperforms all agents being observed. In our experiments, the performance of nearly all agents increased after augmenting with composite strategies.
  • Automated Planning. In many planning problems such as robotic task planning and web service composition, an agent may need to acquire external information during planning. An important issue in planning with external information is information volatility---the collected information may change or expire before the termination of the planning process. For example, a web-based trip planner can generate incorrect travel plans if it does not know the information provided by an airline company has changed. To address this problem, we proposed several query management strategies for handling volatile data, and theoretically analyzed the conditions under which the solutions returned by the planning process remain valid.
Selected Publications:
  • D. Lee, Q. Lu and T.-C. Au. Multiple-Place Swarm Foraging with Dynamic Robot Chains. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2021. To appear.
  • T.-C. Au. Extending the Range of Drone-based Delivery Services by Exploration. In arXiv, 2020.
  • D. Lee and T.-C. Au. Scheduling of Mobile Workstations for Overlapping Production Time and Delivery Time. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. [pdf] [bibtex] [video].
  • T. V. Nguyen and T.-C. Au. A Constant-Time Algorithm for Checking Reachability of Arrival Times and Arrival Velocities of Autonomous Vehicles. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV), 2019. [pdf] [bibtex].
  • J. Im, C. Yoo, D. Cho, K. Kim, J. Lee, D.-H. Cha and T.-C. Au. Deep learning-based monitoring and forecast of the intensity of tropical cyclones. In Proceedings of the IEEE Geoscience and Remote Sensing Society (IGARSS), 2019.
  • H. Moon, J. Martinez-Carranza, T. Cieslewski, M. Faessler, D. Falanga, A. Simovic, D. Scaramuzza, S. Li, M. Ozo, C. D. Wagter, G. d. Croon, S. Hwang, S. Jung, H. Shim, H. Kim, M. Park, T.-C. Au, G. Lee, and S. J. Kim. Autonomous Drone Racing: Challenges and Implemented Technologies. In Intelligent Service Robotics (JIST), 2019. [pdf] [bibtex].
  • D. Nguyen and T.-C. Au. Learning to Generate Backup Paths in Cooperative Transportation of Human-Robot Teams. In Proceedings of the ICRA Workshop on Robot Teammates Operating in Dynamic, Unstructured Environments (RT-DUNE), 2018.
  • T.-C. Au, B. Banerjee, P. Dasgupta, and P. Stone. Multirobot Systems. In IEEE Intelligent Systems, 2018.
  • T. V. Nguyen, D. Nguyen and T.-C. Au. Learning of Vehicular Performance Models for Longitudinal Motion Planning to Satisfy Arrival Requirements. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. [pdf] [bibtex].
  • T. V. Nguyen and T.-C. Au. Extending the Range of Delivery Drones by Exploratory Learning of Energy Models. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2017. Short paper. [pdf] [bibtex].
  • D. Lee and T.-C. Au. Graph-based Scheduling Algorithms for Mobile Workstations. In Proceedings of IJCAI Workshop on Autonomous Mobile Service Robots, 2016.
  • D. Lee and T.-C. Au. Automatic Configuration of Mobile Conveyor Lines. In Proceedings of the International Conference on Robotics and Automation (ICRA), 2016. [pdf] [bibtex].
  • T. V. Nguyen and T.-C. Au. Instance-based Learning of Vehicular Performance Models. In Proceedings of IROS Workshop on Machine Learning in Planning and Control of Robot Motion (MLPC), 2015.
  • T.-C. Au and T. V. Nguyen. Augmented Motion Plans for Planning in Uncertain Terrains. In IJCAI International Workshop on Planning and Scheduling for Space (IWPSS), pp. 2-7, 2015. [pdf] [bibtex].
  • T. V. Nguyen and T.-C. Au. Motion Planning for Arrival Time and Velocity Requirements on Non-Homogeneous Terrains. In ICAPS Workshop on Planning and Robotics (PlanRob), 2015.
  • T.-C. Au, S. Zhang, and P. Stone. Autonomous Intersection Management for Semi-Autonomous Vehicles. In Handbook of Transportation, Routledge, Taylor & Francis Group, 2015. [pdf] [bibtex].
  • T.-C. Au, S. Zhang, and P. Stone. Semi-Autonomous Intersection Management. In Proceedings of the Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1451-1452, 2014. Short paper. [pdf] [bibtex].
  • T.-C. Au, S. Zhang, and P. Stone. Intersection Management with Constraint-based Reservation Systems. In Proceedings of AAMAS 2014 Workshop on Autonomous Robots and Multirobot Systems (ARMS), 2014.
  • T.-C. Au, C.-L. Fok, S. Vishwanath, C. Julien, and P. Stone. Evasion Planning for Autonomous Vehicles at Intersections. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), 2012. [pdf] [bibtex].
  • C.-L. Fok, M. Hanna, S. Gee, T.-C. Au, P. Stone, C. Julien, and S. Vishwanath. A Platform for Evaluating Autonomous Intersection Management Policies. In ACM/IEEE Third International Conference on Cyber-Physical Systems (ICCPS), pp. 87-96, 2012. [pdf] [bibtex].
  • T.-C. Au, M. Quinlan, and P. Stone. Setpoint Scheduling for Autonomous Vehicle Controllers. In IEEE International Conference on Robotics and Automation (ICRA), pp. 2055-2060, 2012. [pdf] [bibtex].
  • T.-C. Au, N. Shahidi, and P. Stone. Improving Transportation Efficiency for Sustainable Society by Autonomous Traffic Management. In Sustainability at UT Austin 2011 Symposium, The University of Texas at Austin, September 2011. [pdf] [bibtex].
  • M. Hausknecht, T.-C. Au, P. Stone, D. Fjardo, and S. T. Waller. Dynamic Lane Reversal in Autonomous Traffic Management. In IEEE Intelligent Transportation Systems Conference (ITSC 2011). [pdf] [bibtex].
  • M. Hausknecht, T.-C. Au, and P. Stone. Autonomous Intersection Management: Multi-Intersection Optimization. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011). [pdf] [bibtex].
  • T.-C. Au, N. Shahidi, and P. Stone. Enforcing Liveness in Autonomous Traffic Management. In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), pp. 1561-1564, August 2011. [pdf] [bibtex] [proofs].
  • D. Fajardo, T.-C. Au, S. T. Waller, P. Stone, and D. Yang. Automated Intersection Control: Performance of a Future Innovation Versus Current Traffic Signal Control. In Transportation Research Record : Journal of the Transportation Research Board, 2259, pp. 223-232, 2012. [pdf] [bibtex].
  • N. Shahidi, T.-C. Au, and P. Stone. Batch Reservations in Autonomous Intersection Management. In Proceedings of the Tenth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), May 2011. Short paper. [pdf] [bibtex].
  • T.-C. Au and P. Stone. Motion Planning Algorithms for Autonomous Intersection Management. In AAAI 2010 Workshop on Bridging The Gap Between Task And Motion Planning (BTAMP), 2010. [pdf] [bibtex].
  • M. Quinlan, T.-C. Au, J. Zhu, N. Stiurca, and P. Stone. Bringing Simulation to Life: A Mixed Reality Autonomous Intersection. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), October 2010. [pdf] [bibtex] [video]. (The corresponding workshop paper in ICAPS-10: [pdf])
  • T.-C. Au, U. Kuter, and D. Nau. Planning for Interactions among Autonomous Agents. In International Workshop on Programming Multi-Agent Systems (ProMAS), 2009. [pdf] [bibtex].
  • T.-C. Au, S. Kraus, and D. Nau. Synthesis of Strategies from Interaction Traces. In Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS'08), pp. 855-862, May 2008. [pdf] [data] [bibtex].
  • T.-C. Au and D. Nau. Is it Accidental or Intentional? A Symbolic Approach to the Noisy Iterated Prisoner's Dilemma. The Iterated Prisoners' Dilemma: 20 Years on, pp.231-262, World Scientific, 2007. [pdf] [bibtex].
  • T.-C. Au, S. Kraus, and D. Nau. Symbolic noise detection in the noisy iterated chicken game and the noisy iterated battle of the sexes. In First International Conference on Computational Cultural Dynamics (ICCCD-2007), pp. 16-25, August 2007. [pdf] [bibtex].
  • T.-C. Au. Dynamic Programming with Stochastic Opponent Models in Social Games: A Preliminary Report. In First International Conference on Computational Cultural Dynamics (ICCCD-2007), pp. 9-15, August 2007. [pdf] [bibtex].
  • T.-C. Au and D. Nau. Reactive Query Policies: A Formalism for Planning with Volatile External Information. IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 243-250, 2007. [pdf] [bibtex].
  • T.-C. Au and D. Nau. The Incompleteness of Planning with Volatile External Information. In Proceedings of the European Conference on Artificial Intelligence (ECAI), August 2006. [pdf] [bibtex].
  • T.-C. Au and D. Nau. Maintaining Cooperation in Noisy Environments. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06). NECTAR paper. pp. 1561-1564, 2006. [pdf] [bibtex].
  • T.-C. Au and D. Nau. Accident or Intention: That is the Question (in the Noisy Iterated Prisoner's Dilemma). In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS'06). pp. 561-568, May 2006. [pdf] [bibtex].
  • T.-C. Au, U. Kuter and D. Nau. Web Service Composition with Volatile Information. In Proceedings of the 4th International Semantic Web Conference (ISWC-2005), pp. 52-66, 2005. [pdf] [bibtex].
  • D. Nau, T.-C. Au, O. Ilghami, U. Kuter, H. Muñoz-Avila, J. W. Murdock, D. Wu, and F. Yaman. Applications of SHOP and SHOP2. IEEE Intelligent Systems 20:2, pp. 34-41, 2005. [pdf] [html] [bibtex].
  • T.-C. Au, D. Nau, and V. Subrahamanian. Utilizing volatile external information during planning. In Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 647-651, August 2004. [pdf] [bibtex].
  • D. Nau, T.-C. Au, O. Ilghami, U. Kuter, J. W. Murdock, D. Wu, and F. Yaman. SHOP2: An HTN planning system. Journal of Artificial Intelligence Research (JAIR) 20:379-404, December 2003. [pdf] [html] [bibtex].
  • T.-C. Au, H. Muñoz-Avila, and D. S. Nau. On the complexity of plan adaptation by derivational analogy in a universal classical planning framework. In Proceedings of the European Conference on Case-Based Reasoning (ECCBR), pp. 13-27, September 4-7 2002. [pdf] [bibtex].
Contact Information:
  • Email: chiu@unist.ac.kr
  • Phone: +82-52-217-2138
  • Skype ID: chiu.au
  • Web page: https://ai.unist.ac.kr/~chiu
  • Address:
    School of Electrical and Computer Engineering (ECE)
    Ulsan National Institute of Science and Technology (UNIST)
    Room 701-6, Building 106
    UNIST-gil 50, Eonyang-eup, Ulju-gun
    Ulsan, Republic of Korea, 44919