Search Results for author: Maximilian Durner

Found 10 papers, 6 papers with code

Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly

2 code implementations3 Jul 2023 Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel

Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i. e. whether they are feasible or not, to circumvent potential efficiency degradation.

Out of Distribution (OOD) Detection

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

no code implementations23 Mar 2023 Maximilian Ulmer, Maximilian Durner, Martin Sundermeyer, Manuel Stoiber, Rudolph Triebel

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.

6D Pose Estimation using RGB

Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning

2 code implementations17 Mar 2023 Matan Atad, Jianxiang Feng, Ismael Rodríguez, Maximilian Durner, Rudolph Triebel

With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner.

Graph Representation Learning

Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks

no code implementations27 Sep 2021 Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton, Ferenc Balint-Benczedi, Rudolph Triebel

This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications.

Domain Adaptation

Bayesian Active Learning for Sim-to-Real Robotic Perception

no code implementations23 Sep 2021 Jianxiang Feng, JongSeok Lee, Maximilian Durner, Rudolph Triebel

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.

Active Learning Informativeness +1

Unknown Object Segmentation from Stereo Images

2 code implementations11 Mar 2021 Maximilian Durner, Wout Boerdijk, Martin Sundermeyer, Werner Friedl, Zoltan-Csaba Marton, Rudolph Triebel

This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map.

Instance Segmentation Object +2

"What's This?" -- Learning to Segment Unknown Objects from Manipulation Sequences

1 code implementation6 Nov 2020 Wout Boerdijk, Martin Sundermeyer, Maximilian Durner, Rudolph Triebel

Furthermore, while the motion of the manipulator and the object are substantial cues for our algorithm, we present means to robustly deal with distraction objects moving in the background, as well as with completely static scenes.

Foreground Segmentation Object +2

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