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Maximilian Ulmer

5 accepted papers

2026

Markerless Robot Detection and 6D Pose Estimation for Multi-Agent SLAM

Markus Rueggeberg, Maximilian Ulmer, Maximilian Durner, Wout Boerdijk, Marcus Gerhard Müller, Rudolph Triebel +1
ICRA 2026poster

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. Loop closure detection between perceptual inputs of different robotic agents is easily compromised in the context of p…

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2025

Conditional Latent Diffusion Models for Zero-Shot Instance Segmentation

Maximilian Ulmer, Wout Boerdijk, Rudolph Triebel, Maximilian Durner
ICCV 2025poster

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 latent diffusion framework that generates instance masks by conditioning the generative process on object templates and im…

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2023

6D Object Pose Estimation from Approximate 3D Models for Orbital Robotics

Maximilian Ulmer, Maximilian Durner, Martin Sundermeyer, Manuel Stoiber, Rudolph Triebel
IROS 2023poster

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 achieve this, we employ a dense 2D-to-3D correspondence predictor that regresses 3D model coordinates for every pixel.…

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2022

Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning

Elie Aljalbout, Maximilian Ulmer, Rudolph Triebel
ICRA 2022poster

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 terms of sample efficiency, especially when using image observations. This has led to increased attention on integrating…

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2020

Learning Vision-based Reactive Policies for Obstacle Avoidance

Elie Aljalbout, Ji Chen, Konstantin Ritt, Maximilian Ulmer, Sami Haddadin
CoRL 2020

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 work in the field aims at improving one of those aspects, we provide a unified framework for approaching this problem.

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