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 | 215 | 2019 |
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 | 142 | 2017 |
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 | 140 | 2015 |
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 | 127 | 2019 |
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 | 120 | 2020 |
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 | 116 | 2018 |
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 | 71 | 2021 |
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 | 57 | 2021 |
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 | 46 | 2021 |
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 | 33 | 2023 |
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 | 30 | 2015 |
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 | 24 | 2016 |
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 | 23 | 2020 |
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 | 21 | 2017 |
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 | 21 | 2015 |
Learning navigation behaviors end to end HTL Chiang, A Faust, M Fiser, A Francis CoRR, 2018 | 18 | 2018 |
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 | 14 | 2020 |
Improved bounds for eigenpath traversal HT Chiang, G Xu, RD Somma Physical Review A 89 (1), 012314, 2014 | 14 | 2014 |
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 | 10 | 2019 |
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 | 7 | 2016 |