Folgen
Hao-Tien (Lewis) Chiang
Hao-Tien (Lewis) Chiang
Waymo Research
Bestätigte E-Mail-Adresse bei google.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
2302019
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
1552017
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
1442015
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
1382019
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
1332020
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
1262018
Scene transformer: A unified architecture for predicting future trajectories of multiple agents
J Ngiam, V Vasudevan, B Caine, Z Zhang, HTL Chiang, J Ling, R Roelofs, ...
International Conference on Learning Representations, 2021
932021
Language to Rewards for Robotic Skill Synthesis
W Yu, N Gileadi, C Fu, S Kirmani, KH Lee, MG Arenas, HTL Chiang, ...
arXiv preprint arXiv:2306.08647, 2023
752023
Scene transformer: A unified multi-task model for behavior prediction and planning
J Ngiam, B Caine, V Vasudevan, Z Zhang, HTL Chiang, J Ling, R Roelofs, ...
arXiv preprint arXiv:2106.08417 2 (7), 2021
662021
Scene transformer: A unified architecture for predicting multiple agent trajectories
J Ngiam, B Caine, V Vasudevan, Z Zhang, HTL Chiang, J Ling, R Roelofs, ...
arXiv preprint arXiv:2106.08417, 2021
622021
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: Selected Contributions of the …, 2015
312015
Safety, challenges, and performance of motion planners in dynamic environments
HT Chiang, B HomChaudhuri, L Smith, L Tapia
Robotics Research: The 18th International Symposium ISRR, 793-808, 2020
252020
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
242017
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
242016
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
222015
Learning navigation behaviors end to end
HTL Chiang, A Faust, M Fiser, A Francis
CoRR, 2018
202018
Fast swept volume estimation with deep learning
HTL Chiang, A Faust, S Sugaya, L Tapia
Algorithmic Foundations of Robotics XIII: Proceedings of the 13th Workshop …, 2020
152020
Improved bounds for eigenpath traversal
HT Chiang, G Xu, RD Somma
Physical Review A 89 (1), 012314, 2014
142014
Principles and guidelines for evaluating social robot navigation algorithms
A Francis, C Pérez-d'Arpino, C Li, F Xia, A Alahi, R Alami, A Bera, ...
arXiv preprint arXiv:2306.16740, 2023
112023
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
112019
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20