Objective learning from human demonstrations
Researchers in biomechanics, neuroscience, human–machine interaction and other fields
are interested in inferring human intentions and objectives from observed actions. The …
are interested in inferring human intentions and objectives from observed actions. The …
Robust nonlinear control of blood glucose in diabetic patients subject to model uncertainties
Recent advances in the artificial pancreas system provide an emerging treatment option for
type 1 diabetes. The performance of the blood glucose regulation directly relies on the …
type 1 diabetes. The performance of the blood glucose regulation directly relies on the …
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 …
[HTML][HTML] A learning from demonstration framework for adaptive task and motion planning in varying package-to-order scenarios
Current advances in Task and Motion Planning (TAMP) framework often rely on a specific
and static task structure. A task structure is a sequence of how work pieces should be …
and static task structure. A task structure is a sequence of how work pieces should be …
Using human ratings for feedback control: A supervised learning approach with application to rehabilitation robotics
This article presents a method for tailoring a parametric controller based on human ratings.
The method leverages supervised learning concepts in order to train a reward model from …
The method leverages supervised learning concepts in order to train a reward model from …
Fast approximate multioutput gaussian processes
Gaussian processes regression models are an appealing machine learning method as they
learn expressive nonlinear models from exemplar data with minimal parameter tuning and …
learn expressive nonlinear models from exemplar data with minimal parameter tuning and …
Understanding Feedforward–Feedback Controller Components In Human Movement Through Optimization-Based Approaches
M Parsapour - 2023 - uwspace.uwaterloo.ca
Despite many studies in human motion analysis using optimal control theory to understand
how movement is generated, less attention is focused on the structure of the optimal …
how movement is generated, less attention is focused on the structure of the optimal …
[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 …