19 citations · 38 across the 11 of their papers we have counts for
11 papers
Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning
Keuntaek Lee, David Isele, Evangelos A. Theodorou +1
It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate…
Adaptive Risk Sensitive Model Predictive Control with Stochastic Search
Ziyi Wang, Oswin So, Keuntaek Lee +2
We present a general framework for optimizing the Conditional Value-at-Risk for dynamical systems using stochastic search. The framework is capable of handling the uncertainty from…
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning
Keuntaek Lee, Bogdan Vlahov, Jason Gibson +2
In this work, we present a method for obtaining an implicit objective function for vision-based navigation. The proposed methodology relies on Imitation Learning, Model Predictive…
Sample-based Distributional Policy Gradient
Rahul Singh, Keuntaek Lee, Yongxin Chen
Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the key idea of…
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC
Keuntaek Lee, Jason Gibson, Evangelos A. Theodorou
Recently, vision-based control has gained traction by leveraging the power of machine learning. In this work, we couple a model predictive control (MPC) framework to a visual pipel…
Deep Forward-Backward SDEs for Min-max Control
Ziyi Wang, Keuntaek Lee, Marcus A. Pereira +2
This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems. The proposed approach relies on the nonlinear Feynman-Kac theorem tha…
Perceptual Attention-based Predictive Control
Keuntaek Lee, Gabriel Nakajima An, Viacheslav Zakharov +1
In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing arch…
Ensemble Bayesian Decision Making with Redundant Deep Perceptual Control Policies
Keuntaek Lee, Ziyi Wang, Bogdan I. Vlahov +2
This work presents a novel ensemble of Bayesian Neural Networks (BNNs) for control of safety-critical systems. Decision making for safety-critical systems is challenging due to per…
Propagating Uncertainty through the tanh Function with Application to Reservoir Computing
Manan Gandhi, Keuntaek Lee, Yunpeng Pan +1
Many neural networks use the tanh activation function, however when given a probability distribution as input, the problem of computing the output distribution in neural networks w…
Safe end-to-end imitation learning for model predictive control
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 circumstance…
Agile Autonomous Driving using End-to-End Deep Imitation Learning
Yunpeng Pan, Ching-An Cheng, Kamil Saigol +4
We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with adva…