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.
1 code implementation • 2 Oct 2022 • Ye-Ji Mun, Masha Itkina, Shuijing Liu, Katherine Driggs-Campbell
To the best of our knowledge, this work is the first to use social occlusion inference for crowd navigation.
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 • 21 Dec 2021 • Pulkit Katdare, Shuijing Liu, Katherine Driggs-Campbell
We also show that the our method is able to estimate performance of a 7 DOF robotic arm using the simulator and remotely collected data from the robot in the real world.
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.
no code implementations • 11 Dec 2017 • Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, Girish Chowdhary
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks.