A Comprehensive Study on Self-Learning Methods and Implications to Autonomous Driving
J ** robots learn new
skills. Numerous papers have presented methods of LfD with good performance in robotics …
skills. Numerous papers have presented methods of LfD with good performance in robotics …
A learning-based personalized driver model using bounded generalized Gaussian mixture models
W Wang, J **, JK Hedrick - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
Individual driver's driving behavior plays a pivotal role in personalized driver assistance
systems. Gaussian mixture models (GMM) have been widely used to fit driving data, but …
systems. Gaussian mixture models (GMM) have been widely used to fit driving data, but …
Inverse learning for data-driven calibration of model-based statistical path planning
This paper presents a method for inverse learning of a control objective defined in terms of
requirements and their joint probability distribution from data. The probability distribution …
requirements and their joint probability distribution from data. The probability distribution …
Eliciting Risk Aversion with Inverse Reinforcement Learning via Interactive Questioning
This paper proposes a novel framework for identifying an agent's risk aversion using
interactive questioning. Our study is conducted in two scenarios: a one-period case and an …
interactive questioning. Our study is conducted in two scenarios: a one-period case and an …
[PDF][PDF] Learning Control Objectives from Human Interactions: Methods and Applications
M Menner - 2020 - research-collection.ethz.ch
This thesis investigates the design of control objectives for the automatic feedback control of
dynamical systems. In particular, it presents methodologies—in addition to their applications …
dynamical systems. In particular, it presents methodologies—in addition to their applications …