Diplomandenseminar

Die AG Robotik von der Universität Bremen bietet in Kooperation mit dem Robotics Innovation Center von der DFKI GmbH eine ganze Reihe von Seminaren und Vorträgen an. In dieser Reihe sind die Bachelor-, Master- und Diplomarbeiten enthalten. Die Termine werden hier regelmäßig aktualisiert. Externe können in vielen Fällen nach Rücksprache mit dem Sekretariat an den Vorträgen teilnehmen.

Undergraduated Seminar

 -  01:00 pm
Kolloquium

Enhancing Human-Robot Dialogue with Visual Voice Activity Detection

from: Arunima Gopikrishnan  (Universität Bremen)
Colloquium
 -  01:30 pm
Zwischenbericht

Unified model for Grasp stability estimation

from: Prithvi Sanghamreddy  (Universität Bremen)
Grasp stability is one of the main concerns in the field of robotics, as it directly impacts the effectiveness and safety of robotic manipulation tasks. Grasping an object and evaluating the grasp is an important task when we use robotic hands. Establishing robotic hands that can securely hold and m…
 -  01:00 pm
Exposé

Design Optimization of the RH5 Humanoid Robot for Reinforcement Learning-Driven Pull-Up Performance

from: Tianyi Jin  (RWTH Aachen)
This thesis aims to advance the design optimization of the RH5 humanoid robot to enhance performance in high-effort tasks, specifically focusing on the pull-up exercise to evaluate upper body strength and endurance. The research addresses existing limitations in upper-body research of humanoid robot…
 -  01:00 pm
Exposé

Entwicklung und Regelung eines zweiarmigen Oberkörper-Klimmzugroboters

from: Ahmad Baker  (Universität Bremen)
In den letzten Jahren haben humanoide Roboter große Fortschritte in der dynamischen Regelung und der Integration drehmomentstarker Elektromotoren mit geringer Getriebeuntersetzung gemacht. Neue Entwicklungen erlauben ihnen komplexe Bewegungen, die eine hohe Leistungsdichte, Koordination und Präzisio…
 -  03:30 pm
Kolloquium

Learning State Transition Functions of a Hydraulic Excavator using Data Driven Deep Learning

from: Jayanth Somashekaraiah  (Universität Bremen)
In this thesis, a neural network model is trained to learn the state transition function of a hydraulic excavator arm or a crane using data driven approach. The excavator arm or crane exhibits highly non linear behaviour due to hydraulic coupling between its actuators, non linearities in valves, par…
 -  01:00 pm
Kolloquium

Adversary-driven Exploration in Coordinative Multi-Agent Deep Reinforcement Learning

from: Tim Schnauder  (Universität Bremen)
Multi-agent deep reinforcement learning (MADRL) is a powerful approach that enables agents to coordinate and collaborate to solve complex collaborative tasks. Applications of MADRL range from robotics to traffic control and resource allocation. However, the learning process can be slow, especially w…
 -  01:00 pm
Kolloquium

Enhancement and Evaluation of an AUV's Docking Station Detection with Deep-Learning Based Object-Detection

from: Faraz Ahmad  (Constructor University)
Autonomous docking for underwater vehicles, especially locating the docking station, presents significant challenges for deploying sub-sea resident AUVs in exploration and monitoring tasks. To extend a fiducial marker-based docking station detection,  use of the state-of-the-art object detectio…
 -  02:00 pm
Exposé

Meta-Reinforcement Learning for Enhanced Generalization in Robotics

from: Lennart Heinbokel  (Universität Bremen)
This thesis aims to investigate the use of meta-reinforcement learning (meta-RL) to enhance sample efficiency in robotic systems, enabling agents to adapt to unseen tasks with fewer interactions. By employing contextual Markov Decision Processes (cMDPs) to formalize task variability, the study offer…
 -  01:00 pm
Kolloquium

Performance Evaluation of Quadratic Programming Solvers for Legged Robots on different Computer Architectures

from: Jakob Middelberg  (Universität Bremen)
In the past decade, the development of dynamic legged robots has gained significant traction and has produced remarkable results. Modern bipeds and quadrupeds are able to traverse difficult terrain while using various gaits and movements like walking, trotting, or jumping. Controlling those robotic …
 -  02:00 pm
Kolloquium

Adaptive Footstep Planning for Model Predictive Control of a Quadruped Robot

from: Hannah Isermann  (Universität Bielefeld)
Colloquium
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last updated 06.09.2024