6 citations · 8 across the 8 of their papers we have counts for
8 papers
Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM
Markus Rueggeberg, Maximilian Ulmer, Maximilian Durner +4
The capability of multi-robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association…
Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation
Maximilian Ulmer, Wout Boerdijk, Rudolph Triebel +1
This paper presents OC-DiT, a novel class of diffusion models designed for object-centric prediction, and applies it to zero-shot instance segmentation. We propose a conditional la…
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
Maximilian Ulmer, Leonard Klüpfel, Maximilian Durner +1
We investigate the efficacy of data augmentations to close the domain gap in spaceborne computer vision, crucial for autonomous operations like on-orbit servicing. As the use of co…
6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics
Maximilian Ulmer, Maximilian Durner, Martin Sundermeyer +2
We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model. To…
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space
Maximilian Ulmer, Elie Aljalbout, Sascha Schwarz +1
Intelligent agents must be able to think fast and slow to perform elaborate manipulation tasks. Reinforcement Learning (RL) has led to many promising results on a range of challeng…
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising approach to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in t…
Making Curiosity Explicit in Vision-based RL
Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising technique to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in…
Learning Vision-based Reactive Policies for Obstacle Avoidance
Elie Aljalbout, Ji Chen, Konstantin Ritt +2
In this paper, we address the problem of vision-based obstacle avoidance for robotic manipulators. This topic poses challenges for both perception and motion generation. While most…