no code implementations • 25 Mar 2024 • Neeloy Chakraborty, Melkior Ornik, Katherine Driggs-Campbell
The rise of foundation models trained on multiple tasks with impressively large datasets from a variety of fields has led researchers to believe that these models may provide common sense reasoning that existing planners are missing.
no code implementations • 1 Aug 2023 • Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell
To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP.
no code implementations • 17 Feb 2023 • Aamir Hasan, Neeloy Chakraborty, Cathy Wu, Katherine Driggs-Campbell
The effects of traffic congestion are widespread and are an impedance to everyday life.
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.
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.