no code implementations • 17 Jan 2022 • Keuntaek Lee, David Isele, Evangelos A. Theodorou, Sangjae Bae
It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants.
no code implementations • 2 Sep 2020 • Ziyi Wang, Oswin So, Keuntaek Lee, Camilo A. Duarte, Evangelos A. Theodorou
We present a general framework for optimizing the Conditional Value-at-Risk for dynamical systems using stochastic search.
Distributional Reinforcement Learning Optimization and Control Robotics
no code implementations • 17 Apr 2020 • Keuntaek Lee, Bogdan Vlahov, Jason Gibson, James M. Rehg, Evangelos A. Theodorou
In this work, we present a method for obtaining an implicit objective function for vision-based navigation.
no code implementations • 8 Jan 2020 • Rahul Singh, Keuntaek Lee, Yongxin Chen
It relies on the key idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards.
no code implementations • 7 Jan 2020 • Keuntaek Lee, Jason Gibson, Evangelos A. Theodorou
In this work, we couple a model predictive control (MPC) framework to a visual pipeline.
no code implementations • 11 Jun 2019 • Ziyi Wang, Keuntaek Lee, Marcus A. Pereira, Ioannis Exarchos, Evangelos A. Theodorou
This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems.
no code implementations • 26 Apr 2019 • Keuntaek Lee, Gabriel Nakajima An, Viacheslav Zakharov, Evangelos A. Theodorou
The proposed information processing architecture is used to support a perceptual attention-based predictive control algorithm that leverages model predictive control (MPC), convolutional neural networks (CNNs), and uncertainty quantification methods.
no code implementations • 25 Jun 2018 • Manan Gandhi, Keuntaek Lee, Yunpeng Pan, Evangelos Theodorou
In this work, we contribute two new methods to propagate uncertainty through the tanh activation function and propose the Probabilistic Echo State Network (PESN), a method that is shown to have better average performance than deterministic Echo State Networks given the random initialization of reservoir states.
no code implementations • 27 Mar 2018 • Keuntaek Lee, Kamil Saigol, Evangelos A. Theodorou
We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set.
no code implementations • 21 Sep 2017 • Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos Theodorou, Byron Boots
We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors.
Robotics