1 code implementation • 13 Jul 2023 • Shuijing Liu, Aamir Hasan, Kaiwen Hong, Runxuan Wang, Peixin Chang, Zachary Mizrachi, Justin Lin, D. Livingston McPherson, Wendy A. Rogers, Katherine Driggs-Campbell
Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language.
2 code implementations • 3 Mar 2022 • Shuijing Liu, Peixin Chang, Zhe Huang, Neeloy Chakraborty, Kaiwen Hong, Weihang Liang, D. Livingston McPherson, Junyi Geng, Katherine Driggs-Campbell
We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.
1 code implementation • 14 Sep 2021 • Shuijing Liu, Peixin Chang, Haonan Chen, Neeloy Chakraborty, Katherine Driggs-Campbell
Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning.
1 code implementation • 7 Sep 2021 • Peixin Chang, Shuijing Liu, D. Livingston McPherson, Katherine Driggs-Campbell
Previous methods rely on a large number of labels and task-specific reward functions.
2 code implementations • 9 Nov 2020 • Shuijing Liu, Peixin Chang, Weihang Liang, Neeloy Chakraborty, Katherine Driggs-Campbell
Safe and efficient navigation through human crowds is an essential capability for mobile robots.
no code implementations • 19 Sep 2019 • Peixin Chang, Shuijing Liu, Haonan Chen, Katherine Driggs-Campbell
We explore the interpretation of sound for robot decision making, inspired by human speech comprehension.