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Hao-Tien (Lewis) Chiang

PhD Candidate

Tapia Lab - Department of Computer Science

Mailing address:

Mail stop: MSC01 1100
1 University of New Mexico
University of New Mexico
Albuquerque, NM 87131-0001 USA

Contact:

E-Mail:
Office: 3400A Farris
Phone: 917-912-6102
Fax: 505.277.6927

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 Lab and I'm 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 and object oriented programming blew my mind), I then transfered to the PhD program in Computer Science at UNM in Spring 2015.

Since joining CS, I've been working on bring robots to our everyday life. To do so, we need novel algorithms to generate safe and efficient robot motions (so it doesn't flip tables or run into people) in dynamic, noisy and unstructured environments. 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 these rich, yet drastically different paradigms.

In one 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 [Chiang et. al. T-RO 17, Chiang et. al. ICRA 17].

In another example, we combined machine learning with motion planning by approximating the expensive swept volume computations by deep neural networks. This revolutionary distance measure significantly increases the efficiency and solution quality of sampling-based motion planning [Chiang et. al. WAFR 18].

Lastly, thanks to my experience at Google Brain, we combined all three paradigms in RL-RRT [Chiang et. al. RA-L 18]. This method uses deep reinforcement learning to navigate locally for robots with complex dynamics (e.g., spacecrafts and surface ships) in noisy environments. Next, we used sampling-based motion planners to guide the learned policy in order to achive rapid, global exploration. This allowed us to automatically and efficiently generate safe motions for robots with complex dynamics.

Overall, by combining the three paradigms, we were able to address many important challenges that currently limit robots to controlled lab spaces. We aim to continue combining Learning, Planning and Control in order to address major robotics issues such as the sim-to-real gap, data-efficiency, uncertainty, social compliance and trust worthiness.

A detailed list of my 7 journal and 10 conference papers can be found in my CV below.

CV:   pdf






Publications

  • Hao-Tien (Lewis) 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.(pdf, appendix)

  • Hao-Tien (Lewis) 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 (Lewis) 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 (Lewis) 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)

  • Hao-Tien (Lewis) Chiang, Baisravan HomChaudhuri, Abraham Vinod, Meeko Oishi, Lydia Tapia, "Dynamic Risk Tolerance: Motion Planning by Balancing Short-Term and Long-Term Stochastic Dynamic Predictions", In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 1023-1030, Singapore, May 2017. (pdf, Bibtex)

  • 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)