Search Results for author:
Found 10 papers, 0 papers with code
Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning
It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants.
Adaptive Risk Sensitive Model Predictive Control with Stochastic Search
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
Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning
In this work, we present a method for obtaining an implicit objective function for vision-based navigation.
Sample-based Distributional Policy Gradient
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.
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC
In this work, we couple a model predictive control (MPC) framework to a visual pipeline.
Deep Forward-Backward SDEs for Min-max Control
This paper presents a novel approach to numerically solve stochastic differential games for nonlinear systems.
Perceptual Attention-based Predictive Control
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.
Propagating Uncertainty through the tanh Function with Application to Reservoir Computing
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.
Safe end-to-end imitation learning for model predictive control
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.
Imitation Learning for Agile Autonomous Driving
We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors.
Robotics
