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
Zwischenbericht

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…
 -  01:00 pm
Zwischenbericht

Evaluation of combined Few-short and continual learning approaches for Robotic perception

from: Saira Philip  (Universität Bremen)
State-of-the-art robotic learning frameworks struggle in typical real-world scenarios like dynamically changing or unseen environments that require robots to adapt quickly (few-shot and meta-learning). Furthermore, real-world settings require robots to continually learn without forgetting what they …
 -  01:00 pm
Zwischenbericht

Do different Quantum Optimal Control algorithms have an influence on the fidelity of one-qubit and two-qubit gates on superconducting systems?

from: Adrian Mellentin  (Universität Bremen)
Today’s quantum processor architectures with more than 1000 superconducting qubits are getting closer to the goal of performing complicated calculations. However, the noisy intermediate-scale quantum (NISQ) devices that exist at this time are challenged by phenomena like leakage, cross talking betwe…
 -  02:00 pm
Zwischenbericht

Hybrid-Driven State Estimation for a Humanoid Robot

from: Lasse Hohmeyer  (Universität Bremen)
State estimation refers to all algorithms calculating the internal state of a robot. Since the true state is always unknown, it has to be estimated based on a series of noisy and inaccurate measurements. This is a crucial task in mobile robotics and a more accurate state estimation almost always lea…
 -  01:00 pm
Kolloquium

Enhancing Human-Robot Dialogue with Visual Voice Activity Detection

from: Arunima Gopikrishnan  (Universität Bremen)
This thesis critically examines the integration of Visual Voice Activity Detection (VVAD) technology into human-robot dialogue systems, focusing on its potential to enhance communication in noisy environments. While traditional speech recognition systems excel in quiet settings, their performance de…
 -  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…
© DFKI GmbH
last updated 06.09.2024