Hao-Tien (Lewis) Chiang
PhD Student
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Mail stop: MSC01 11001 University of New Mexico
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Machine learning and robotics scientist with a proven record in research, publication, industry coding and developing motion planning and machine learning algorithms for robotic systems. Passionate with coding and new technologies.
I'm a PhD student here in Tapia Labb and also a Student Researcher @ Google Brain Robotics.
Here's a short bio and introduction to my research:
I got my BS degree in Atmospheric Sciences at the National Taiwan University. I first joined the University of New Mexico as a PhD student in Physics and worked on developing Quantum Algorithms for 3 years.
Due to the love for robotics and coding (I learned Java by myself Object Oriented Programming blew my mind), I then transfered to the PhD program in Computer Science at UNM in Spring 2015 under the supervision of professor Lydia Tapia.
Since joining my PhD program in January 2015, I've been working on bring robots to everyday life. To do that, we need novel algorithms to generate safe and efficient robot motions (so it doesn't flip tables or run into people).
I was very lucky since I learned a bunch in Motion Planning and Control Theory at Tapia Lab and Machine Learning at Google Brain Robotics.
This allows me to combine tools and concepts from Planning, Control and Learning.
For example, we combined Stochastic Reachability from Control Theory with Artificial Potential Fields and Sampling-based Planning from Motion Planning. This allowed us to systematically reason about how to generate robot motion in the presence of obstacle motion uncertainty.
developing methods for planning in dynamic environments.
In one such method, Monte Carlo simulations were used to predict the stochastic motion of obstacles as well as robot sensor noise. These predictions are used to build trees of possible robot motion in order to identify collision-free paths [Chiang et. al. IROS 17, Chiang et. al. IROS 16, Chiang et. al. IROS 15].
In another example, we utilized stochastic reachability analysis, which is a formal method that provides navigation safety guarantees even in the presence of stochastic obstacle motion. However, since the computational cost of this method scales exponentially with the number of obstacles, we combined it with an artificial potential field-based approach in order to avoid collisions in real-time with up to 900 moving obstacles [Malone et. al. T-RO 17, Chiang et. al. WAFR 14, Chiang et. al. ICRA 15].
We also developed a reinforcement learning-based method where the agent is trained to avoid obstacles in a simple environment. The learning result can be transferred to avoid collisions in large complex environments with hundreds of stochastically moving obstacles [Faust et. al. ICRA 16].
CV: pdf
Publications
- Hao-Tien Chiang, Aleksandra Faust, Lydia Tapia, "Fast Swept Volume Estimation with Deep Learning", In Proceedings of the Workshop on Algorithmic Foundations of Robotics (WAFR), Merida, Mexico, Dec. 2018. Published in Algorithmic Foundations of Robotics XIII, Zeist, Springer, 2020(pdf, appendix)
- Hao-Tien Chiang, Aleksandra Faust, Lydia Tapia, "Deep Neural Networks for Swept Volume Prediction Between Configurations", In Proceedings of the Third Workshop on Machine Learning in Planning and Control of Robot Motion Workshop (MLPC 18), IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May. 2018.(pdf)
- Hao-Tien Chiang and Lydia Tapia, "COLREG-RRT: A RRT-based COLREGS-Compliant Motion Planner for Surface Vehicle Navigation", In Proc. of IEEE International Conference on Robotics and Automation (ICRA), to appear, 2018.(pdf)
- Nick Malone, Hao-Tien Chiang, Kendra Lesser, Meeko Oishi, Lydia Tapia, "Hybrid Dynamic Moving Obstacle Avoidance Using a Stochastic Reachable Set-Based Potential Field", IEEE Transactions on Robotics, 33(8), pp. 1124-1138, Oct. 2017. (pdf, Bibtex)
- Hao-Tien Chiang, Baisravan HomChauhudri, Lee Smith, Lydia Tapia, "Safety, Challenges, and Performance of Motion Planners in Dynamic Environments", In 2017 International Symposium of Robotics Research (ISRR), Puerto Varas, Chile, Dec. 2017. (pdf, Bibtex)
- Torin Adamson, Hao-Tien Chiang, Meeko Oishi, Lydia Tapia. "Busy Beeway: A Game for Testing Human-Automation Collaboration for Navigation", In 2017 ACM SIGGRAPH Conference on Motion in Games (MIG), Barcelona, Spain, Nov. 2017. (pdf, Bibtex)
- Avanika Mahajan, Lama A. Youssef, Cédric Cleyrat, Rachel Grattan, Shayna R. Lucero, Christopher P. Mattison, M. Frank Erasmus, Bruna Jacobson, Lydia Tapia, William S. Hlavacek and Mark Schuyler, "Allergen Valency, Dose, and FcεRI Occupancy Set Thresholds for Secretory Responses to Pen a 1 and Motivate Design of Hypoallergens", In The Journal of Immunology, 198(3) pp.1034-1046, Feb. 2017. (pdf)
- Hao-Tien Chiang, Nathanael Rackley, Lydia Tapia, "Runtime SES Planning: Online Motion Planning in Environments with Stochastic Dynamics and Uncertainty", In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4802-4809, Deajon, South Korea, Oct. 2016. (pdf, Bibtex)
- Aleksandra Faust, Hao-Tien Chiang, Nathanael Rackley, Lydia Tapia, "Avoiding Moving Obstacles with Stochastic Hybrid Dynamics using PEARL:PrEference Appraisal Reinforcement Learning", In Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 484-490, Stockholm, Sweeden, May 2016. (pdf, Bibtex)
- Hao-Tien Chiang, Nathanael Rackley, Lydia Tapia, "Stochastic Ensemble Simulation Motion Planning in Stochastic Dynamic Environments", In International Conference on Intelligent Robots and Systems (IROS), pp 2347-2354, Hamburg, Germany, Oct. 2015. (pdf, Bibtex)
- Aleksandra Faust, Hao-Tien Chiang, Nathanael Rackley, Lydia Tapia, "Dynamic Obstacle Avoidance with PEARL: PrEference Appraisal Reinforcement Learning", In International Conference on Robotics and Automation (ICRA), pp. 484-490, Hamburg, Germany, Sept. 2015. (pdf, Bibtex)
- Hao-Tien Chiang, Nick Malone, Kendra Lesser, Meeko Oishi, Lydia Tapia, "Path-Guided Artificial Potential Fields with Stochastic Reachable Sets for Motion Planning in Highly Dynamic Environments", In International Conference on Robotics and Automation (ICRA), pp. 2347-2354, Seattle, WA, U.S.A., May 2015. (pdf, Bibtex)
- Hao-Tien Chiang, Nick Malone, Kendra Lesser, Meeko Oishi, Lydia Tapia, "Aggressive Moving Obstacle Avoidance Using a Stochastic Reachable Set Based Potential Field", In International Workshop on the Algorithmic Foundations of Robotics (WAFR), Istanbul, Turkey, 3-5 Aug. 2014. (pdf, Bibtex)
