no code implementations • 29 Sep 2023 • Luuk van den Bent, Tomás Coleman, Robert Babuska
The method consists of a deep learning-based vision system to first identify the individual trusses in the crate and then determine a suitable grasping location on the stem.
1 code implementation • 16 Sep 2022 • Jiri Sedlar, Karla Stepanova, Radoslav Skoviera, Jan K. Behrens, Matus Tuna, Gabriela Sejnova, Josef Sivic, Robert Babuska
This paper introduces a dataset for training and evaluating methods for 6D pose estimation of hand-held tools in task demonstrations captured by a standard RGB camera.
no code implementations • 1 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.
no code implementations • 24 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.
no code implementations • 25 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.
no code implementations • 29 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.
no code implementations • 8 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.
no code implementations • 1 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.
no code implementations • 21 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.