Hao-Tien (Lewis) Chiang
Hao-Tien (Lewis) Chiang
Waymo Research
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Learning navigation behaviors end-to-end with autorl
HTL Chiang, A Faust, M Fiser, A Francis
IEEE Robotics and Automation Letters 4 (2), 2007-2014, 2019
962019
Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments
HT Chiang, N Malone, K Lesser, M Oishi, L Tapia
2015 IEEE international conference on robotics and automation (ICRA), 2347-2354, 2015
912015
Hybrid dynamic moving obstacle avoidance using a stochastic reachable set-based potential field
N Malone, HT Chiang, K Lesser, M Oishi, L Tapia
IEEE Transactions on Robotics 33 (5), 1124-1138, 2017
752017
COLREG-RRT: an RRT-based COLREGS-compliant motion planner for surface vehicle navigation
HTL Chiang, L Tapia
IEEE Robotics and Automation Letters 3 (3), 2024-2031, 2018
452018
RL-RRT: Kinodynamic motion planning via learning reachability estimators from RL policies
HTL Chiang, J Hsu, M Fiser, L Tapia, A Faust
IEEE Robotics and Automation Letters 4 (4), 4298-4305, 2019
382019
Long-range indoor navigation with prm-rl
A Francis, A Faust, HTL Chiang, J Hsu, JC Kew, M Fiser, TWE Lee
IEEE Transactions on Robotics 36 (4), 1115-1134, 2020
322020
Aggressive moving obstacle avoidance using a stochastic reachable set based potential field
HT Chiang, N Malone, K Lesser, M Oishi, L Tapia
Algorithmic Foundations of Robotics XI, 73-89, 2015
242015
Stochastic ensemble simulation motion planning in stochastic dynamic environments
HT Chiang, N Rackley, L Tapia
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
202015
Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions
HTL Chiang, B HomChaudhuri, AP Vinod, M Oishi, L Tapia
2017 IEEE International Conference on Robotics and Automation (ICRA), 3762-3769, 2017
172017
Avoiding moving obstacles with stochastic hybrid dynamics using pearl: Preference appraisal reinforcement learning
A Faust, HT Chiang, N Rackley, L Tapia
2016 IEEE International Conference on Robotics and Automation (ICRA), 484-490, 2016
172016
Safety, challenges, and performance of motion planners in dynamic environments
HTL Chiang, B HomChaudhuri, L Smith, L Tapia
Robotics Research, 793-808, 2020
142020
Learning navigation behaviors end to end
HL Chiang, A Faust, M Fiser, A Francis
CoRR, 2018
122018
Improved bounds for eigenpath traversal
HT Chiang, G Xu, RD Somma
Physical Review A 89 (1), 012314, 2014
102014
Fast swept volume estimation with deep learning
HTL Chiang, A Faust, S Sugaya, L Tapia
82018
Comparison of deep reinforcement learning policies to formal methods for moving obstacle avoidance
A Garg, HTL Chiang, S Sugaya, A Faust, L Tapia
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
42019
Pearl: Preference appraisal reinforcement learning for motion planning
A Faust, HTL Chiang, L Tapia
arXiv preprint arXiv:1811.12651, 2018
42018
Runtime SES planning: Online motion planning in environments with stochastic dynamics and uncertainty
HT Chiang, N Rackley, L Tapia
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016
42016
Deep neural networks for swept volume prediction between configurations
HTL Chiang, A Faust, L Tapia
arXiv preprint arXiv:1805.11597, 2018
22018
Dynamic obstacle avoidance with PEARL: Preference appraisal reinforcement learning
A Faust, HT Chiang, N Rackley, L Tapia
Second Annual Machine Learning in Planning and Control of Robot Motion …, 2015
22015
Fast deep swept volume estimator
HTL Chiang, JEG Baxter, S Sugaya, MR Yousefi, A Faust, L Tapia
The International Journal of Robotics Research, 0278364920940781, 2020
12020
The system can't perform the operation now. Try again later.
Articles 1–20