Search Results for author: Robert Babuska

Found 7 papers, 0 papers with code

Geometry-Based Grasping of Vine Tomatoes

no code implementations1 Mar 2021 Taeke de Haan, Padmaja Kulkarni, Robert Babuska

The grasping method then uses a geometric model of the robotic hand and the truss to determine a suitable grasping location on the stem.

GEM: Glare or Gloom, I Can Still See You -- End-to-End Multimodal Object Detection

no code implementations24 Feb 2021 Osama Mazhar, Robert Babuska, Jens Kober

We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions.

2D Object Detection Human robot interaction

DeepKoCo: Efficient latent planning with a task-relevant Koopman representation

no code implementations25 Nov 2020 Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuska

This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images.

Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice

no code implementations29 Nov 2015 Frederik Ruelens, Bert Claessens, Salman Quaiyum, Bart De Schutter, Robert Babuska, Ronnie Belmans

A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation.

Decision Making

Residential Demand Response Applications Using Batch Reinforcement Learning

no code implementations8 Apr 2015 Frederik Ruelens, Bert Claessens, Stijn Vandael, Bart De Schutter, Robert Babuska, Ronnie Belmans

We propose a model-free Monte-Carlo estimator method that uses a metric to construct artificial trajectories and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat.

Sampling-based Approximations with Quantitative Performance for the Probabilistic Reach-Avoid Problem over General Markov Processes

no code implementations1 Sep 2014 Sofie Haesaert, Robert Babuska, Alessandro Abate

This article deals with stochastic processes endowed with the Markov (memoryless) property and evolving over general (uncountable) state spaces.

Reinforcement learning for port-Hamiltonian systems

no code implementations21 Dec 2012 Olivier Sprangers, Gabriel A. D. Lopes, Robert Babuska

The parameters of the control law are found using actor-critic reinforcement learning, enabling learning near-optimal control policies satisfying a desired closed-loop energy landscape.

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