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Hao-Tien Lewis Chiang

9 accepted papers

2024

Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs

Zhuo Xu, Hao-Tien Lewis Chiang, Zipeng Fu, Mithun George Jacob, Tingnan Zhang, Tsang-Wei Edward Lee +16
CoRL 2024poster

An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigation. To achieve this, we study a widely useful category of navigation tasks we call Multimodal Instruction Navigation wi…

Cited by 20Source ↗Scholar ↗
2023

Language to Rewards for Robotic Skill Synthesis

Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montserrat Gonzalez Arenas +14
CoRL 2023oral

Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-le…

Cited by 326Source ↗Scholar ↗
2023

Robots That Can See: Leveraging Human Pose for Trajectory Prediction

Tim Salzmann, Hao-Tien Lewis Chiang, Markus Ryll, Dorsa Sadigh, Carolina Parada, Alex Bewley
RA-L 2023

Anticipating the motion of all humans in dynamic environments such as homes and offices is critical to enable safe and effective robot navigation. Such spaces remain challenging as humans do not follow strict rules of motion and there are often multiple occluded entry points such as corners and door

Cited by 39Source ↗code ↗Scholar ↗
2022

Scene Transformer: A unified architecture for predicting future trajectories of multiple agents

Jiquan Ngiam, Vijay Vasudevan, Benjamin Caine, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling +8
ICLR 2022poster

Predicting the motion of multiple agents is necessary for planning in dynamic environments. This task is challenging for autonomous driving since agents (e.g., vehicles and pedestrians) and their associated behaviors may be diverse and influence one another. Most prior work have focused on predictin…

Cited by 0Source ↗PDF ↗Scholar ↗
2019

Comparison of Deep Reinforcement Learning Policies to Formal Methods for Moving Obstacle Avoidance

Arpit Garg, Hao-Tien Lewis Chiang, Satomi Sugaya, Aleksandra Faust, Lydia Tapia
IROS 2019poster

Deep Reinforcement Learning (RL) has recently emerged as a solution for moving obstacle avoidance. Deep RL learns to simultaneously predict obstacle motions and corresponding avoidance actions directly from robot sensors, even for obstacles with different dynamics models. However, deep RL methods ty…

Cited by 14Source ↗Scholar ↗
2019

RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators From RL Policies

Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser, Lydia Tapia, Aleksandra Faust
RA-L 2019

This letter addresses two challenges facing samplingbased kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly E

Cited by 157Source ↗Scholar ↗
2017

Dynamic risk tolerance: Motion planning by balancing short-term and long-term stochastic dynamic predictions

Hao-Tien Lewis Chiang, Baisravan HomChaudhuri, Abraham P. Vinod, Meeko Oishi, Lydia Tapia
ICRA 2017poster

Identifying collision-free paths over long time windows in environments with stochastically moving obstacles is difficult, in part because long-term predictions of obstacle positions typically have low fidelity, and the region of possible obstacle occupancy is typically large. As a result, planning…

Cited by 25Source ↗Scholar ↗