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Maximilian Durner

17 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…

Cited by 0Source ↗PDF ↗Scholar ↗
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…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Towards Autonomous Data Annotation and System-Agnostic Robotic Grasping Benchmarking with 3D-Printed Fixtures

Wout Boerdijk, Maximilian Durner, Ryo Sakagami, Peter Lehner, Rudolph Triebel
ICRA 2025

The interaction of robots with their environment requires robust object-centric perception capabilities, typically achieved using learning-based methods trained on synthetic data. However, real-world deployment demands evaluating these capabilities in relevant environments, often involving extensive

Cited by 0Source ↗code ↗Scholar ↗
2024

Unknown Object Grasping for Assistive Robotics

Elle Miller, Maximilian Durner, Matthias Humt, Gabriel Quere, Wout Boerdijk, Ashok M. Sundaram +2
ICRA 2024poster

We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of…

Cited by 2Source ↗Scholar ↗
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.…

Cited by 11Source ↗Scholar ↗
2023

Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning

Matan Atad, Jianxiang Feng, Ismael Rodríguez, Maximilian Durner, Rudolph Triebel
IROS 2023poster

Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges in realizing such automation resides in efficiently finding solutions from a gro…

Cited by 12Source ↗code ↗Scholar ↗
2022

Bayesian Active Learning for Sim-to-Real Robotic Perception

Jianxiang Feng, Jongseok Lee, Maximilian Durner, Rudolph Triebel
IROS 2022poster

While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap. In practice, this gap is hard to resolve with only synthetic data. Therefore, we focus on an effi…

Cited by 17Source ↗Scholar ↗
2021

Unknown Object Segmentation from Stereo Images

Maximilian Durner, Wout Boerdijk, Martin Sundermeyer, Werner Friedl, Zoltán-Csaba Márton, Rudolph Triebel
IROS 2021poster

Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known object categories. However, for robots interacting in dynamic and cluttered environments, this is not realistic and seve…

Cited by 40Source ↗code ↗Scholar ↗
2021

“What’s This?” - Learning to Segment Unknown Objects from Manipulation Sequences

Wout Boerdijk, Martin Sundermeyer, Maximilian Durner, Rudolph Triebel
ICRA 2021poster

We present a novel framework for self-supervised grasped object segmentation with a robotic manipulator. Our method successively learns an agnostic foreground segmentation followed by a distinction between manipulator and object solely by observing the motion between consecutive RGB frames. In contr…

Cited by 7Source ↗code ↗Scholar ↗
2020

Multi-Path Learning for Object Pose Estimation Across Domains

Martin Sundermeyer, Maximilian Durner, En Yen Puang, Zoltan-Csaba Marton, Narunas Vaskevicius, Kai O. Arras +1
CVPR 2020poster

We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe an implicit orientation of all objects seen during training, but can also relate views of untrained objects. Our…

Cited by 122PDF ↗code ↗Scholar ↗
2020

Pattern Recognition for Knowledge Transfer in Robotic Assembly Sequence Planning

Ismael Rodríguez, Korbinian Nottensteiner, Daniel Leidner, Maximilian Durner, Freek Stulp, Alin Albu-Schäffer
RA-L 2020

The autonomous assembly of customized products is highly demanded in future manufacturing scenarios. This requires robotic systems being able to adapt to individual products without increasing overall production time. However, increasingly complex assemblies lead to a growing number of potential ass

Cited by 34Source ↗Scholar ↗
2020

Self-Supervised Object-in-Gripper Segmentation from Robotic Motions

Wout Boerdijk, Martin Sundermeyer, Maximilian Durner, Rudolph Triebel
CoRL 2020

Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time consuming and often requires manual labeling. To this end, we propose a simple, yet robust solution for learning to seg

Cited by 0Source ↗PDF ↗Scholar ↗
2020

The ARCHES Space-Analogue Demonstration Mission: Towards Heterogeneous Teams of Autonomous Robots for Collaborative Scientific Sampling in Planetary Exploration

Martin J. Schuster, Marcus Gerhard Müller, Sebastian G. Brunner, Hannah Lehner, Peter Lehner, Ryo Sakagami +32
RA-L 2020

Teams of mobile robots will play a crucial role in future missions to explore the surfaces of extraterrestrial bodies. Setting up infrastructure and taking scientific samples are expensive tasks when operating in distant, challenging, and unknown environments. In contrast to current single-robot spa

Cited by 92Source ↗Scholar ↗
2019

Visual Repetition Sampling for Robot Manipulation Planning

En Yen Puang, Peter Lehner, Zoltan-Csaba Marton, Maximilian Durner, Rudolph Triebel, Alin Albu-Schäffer
ICRA 2019poster

One of the main challenges in sampling-based motion planners is to find an efficient sampling strategy. While methods such as Rapidly-exploring Random Tree (RRT) have shown to be more reliable in complex environments than optimization-based methods, they often require longer planning times, which re…

Cited by 7Source ↗Scholar ↗
2018

Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner, Manuel Brucker, Rudolph Triebel
ECCV 2018poster

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several…

Cited by 699Source ↗PDF ↗code ↗Scholar ↗
2018

Semantic Labeling of Indoor Environments from 3D RGB Maps

Manuel Brucker, Maximilian Durner, Rareş Ambruş, Zoltán Csaba Márton, Axel Wendt, Patric Jensfelt +2
ICRA 2018poster

We present an approach to automatically assign semantic labels to rooms reconstructed from 3D RGB maps of apartments. Evidence for the room types is generated using state-of-the-art deep-learning techniques for scene classification and object detection based on automatically generated virtual RGB vi…

Cited by 30Source ↗Scholar ↗
2017

Selecting CNN features for online learning of 3D objects

Monika Ullrich, Haider Ali, Maximilian Durner, Zoltán-Csaba Márton, Rudolph Triebel
IROS 2017poster

We present a novel method for classifying 3D objects that is particularly tailored for the requirements in robotic applications. The major challenges here are the comparably small amount of available training data and the fact that often data is perceived in streams and not in fixed-size pools. Trad…

Cited by 7Source ↗Scholar ↗