Graduiertenseminar/ Brown Bag Talks

Seite 2 von 23.

 -  14:00 Uhr
DFKI Vortrag
RH1 A 1.03

Phd-Talk: Variable Stiffness Mechanisms

von: Christoph Stoeffler  (DFKI GmbH)
In this talk, the general idea of ``Variable Stiffness Mechanisms'' is presented and a specific design to achieve stiffness modulation in mechanisms is explicated. Following this approach, we want to exploit Parallel Redundant Mechanisms (PRM) in addition with nonlinear springs to obtain controllabl...
 -  13:00 Uhr
Externer Vortrag
RH1 A 1.03

Gast-Vortrag: Modeling and Identification of Robotic Manipulators – A Geometric Approach

von: Prof. Andreas Müller  (JKU, Linz)
Robots are controlled mechanisms, and their analysis and control requires adequately accurate but at the same time efficient models. While in the past, the computational efficacy of the kinematic and dynamic models were addressed, there is a recent interest in efficient and modular modeling procedur...
 -  10:00 Uhr
DFKI Vortrag
RH1 A 1.03

Promotionsvortrag: Evolutionary Legged Robotics

von: Malte Langosz  (DFKI GmbH)
Promotionsvortrag
 -  14:00 Uhr
Externer Vortrag
RH1 A 1.03

Energy Resources, Human Induced Earthquakes and New Research Horizons

von: Prof. Dr. İhsan Engin Bal  (Hanze University of Applied Sciences Groningen, The Netherlands)
Exploitation of energy resources has been of interest to several governments around the world in recent years. Developments in shale gas extraction methods as well as the growing interest towards the use of geothermal energy triggered a collateral issue: earthquakes. Regions housing the energy sourc...
 -  12:00 Uhr
Externer Vortrag
RH1 A 1.03

Model-based metacontrol for autonomous robots

von: Dr. Carlos Hernandez Corbato  (Postdoc at TU Delft and ROS-Industrial coordinator)
In 2016, Team Delft won the Amazon Robotics Challenge, a competition on autonomous manipulation. These competitions reveal the challenge in integrating complex robotic system architectures that need to perform complete tasks in real environments. Methods and tools are needed to build robotic system...
 -  15:00 Uhr
Externer Vortrag
RH1 A 1.03

Ontology in robotics: a perspective

von: Stefano Borgo  (Laboratory for Applied Ontology, ISTC CNR Trento, Italy)
The talk is divided in two parts. In the first we introduce the notion of ontological analysis and foundational ontology and their role in knowledge organization and exploitation. In the second part, we look at how ontology can help artificial agents to achieve and manage commonsense/cognitive know...
 -  12:30 Uhr
DFKI Vortrag
RH5 117

Design and Experimental Evaluation of a Hybrid Wheeled-Leg Exploration Rover in the Context of Multi-Robot Systems

von: Florian Cordes  (DFKI GmbH)
In der vorliegenden kumulativen Dissertation werden das Design, die Implementierung, die Kontrolle und eine experimentelle Evaluation eines neuartigen Rovers mit aktivem Fahrwerk vorgenommen und beschrieben. Durch das hybride Fahrwerk wird eine Basis für vielfältige Lokomotionsformen geschaffen. Da...
 -  15:00 Uhr
Externer Vortrag
RH1 A 1.03

Application of Convolutional Neural Networks for Classification of Adult Mosquitoes in Field

von: Daniel da Silva Motta  (Sistema FIEB SENAI CIMATEC)
This talk is given at a regular meeting of the task force ELLIAS (Embodied Lifelong Learning of Intelligent Autonomous Systems). I would like to use this opportunity to point out to everybody who is generally interested in machine learning and optimization (or everybody who participated in ELLIAS' ...
 -  14:00 Uhr
DFKI Vortrag
RH1 A 1.03

PhD Talk: Learning Coordination and Reactivity of Movement Primitives from Demonstration

von: Christoph Petzoldt  (Universität Bremen)
PhD Talk: Learning Coordination and Reactivity of Movement Primitives from Demonstration Learning from human demonstrations is a promising way to teach complex motor systems novel skills in an intuitive manner without requiring robotic experts. This will be important for future robots cooperating w...
 -  14:00 Uhr
Externer Vortrag
RH1 A 1.03

Deep Reinforcement Learning for Human-Level Agents

von: Dr. Thanh Nguyen  (Deakin University, Australia)
Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, there have been several issues with deep reinforcement learning methods such as the limited exploration capability of learning agents, the ability to integrate human knowledge into l...

Seite 2 von 23.

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zuletzt geändert am 30.07.2019
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