911 citations · 2.9k across the 99 of their papers we have counts for
16 papers · 1 filter
Robust, Compliant Assembly via Optimal Belief Space Planning
Florian Wirnshofer, Philipp S. Schmitt, Wendelin Feiten +2
In automated manufacturing, robots must reliably assemble parts of various geometries and low tolerances. Ideally, they plan the required motions autonomously. This poses a substan…
Accurate Pouring with an Autonomous Robot Using an RGB-D Camera
Chau Do, Wolfram Burgard
Robotic assistants in a home environment are expected to perform various complex tasks for their users. One particularly challenging task is pouring drinks into cups, which for suc…
Learning a Local Feature Descriptor for 3D LiDAR Scans
Ayush Dewan, Tim Caselitz, Wolfram Burgard
Robust data association is necessary for virtually every SLAM system and finding corresponding points is typically a preprocessing step for scan alignment algorithms. Traditionally…
Vision-based Autonomous Landing in Catastrophe-Struck Environments
Mayank Mittal, Abhinav Valada, Wolfram Burgard
Unmanned Aerial Vehicles (UAVs) equipped with bioradars are a life-saving technology that can enable identification of survivors under collapsed buildings in the aftermath of natur…
Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing
Noha Radwan, Wolfram Burgard, Abhinav Valada
For mobile robots navigating on sidewalks, it is essential to be able to safely cross street intersections. Most existing approaches rely on the recognition of the traffic light si…
Self-Supervised Model Adaptation for Multimodal Semantic Segmentation
Abhinav Valada, Rohit Mohan, Wolfram Burgard
Learning to reliably perceive and understand the scene is an integral enabler for robots to operate in the real-world. This problem is inherently challenging due to the multitude o…
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding
Joos Behncke, Robin Tibor Schirrmeister, Martin Völker +5
When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different distributions. Furthermore, di…
The role of robot design in decoding error-related information from EEG signals of a human observer
Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard +1
For utilization of robotic assistive devices in everyday life, means for detection and processing of erroneous robot actions are a focal aspect in the development of collaborative…
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
Oleksii Zhelo, Jingwei Zhang, Lei Tai +2
This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal ext…
Intracranial Error Detection via Deep Learning
Martin Völker, Jiří Hammer, Robin T. Schirrmeister +6
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalo…
The Limits and Potentials of Deep Learning for Robotics
Niko Sünderhauf, Oliver Brock, Walter Scheirer +8
The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning com…
Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification
Abhinav Valada, Wolfram Burgard
Terrain classification is a critical component of any autonomous mobile robot system operating in unknown real-world environments. Over the years, several proprioceptive terrain cl…
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
Noha Radwan, Abhinav Valada, Wolfram Burgard
Semantic understanding and localization are fundamental enablers of robot autonomy that have for the most part been tackled as disjoint problems. While deep learning has enabled re…
3D Human Pose Estimation in RGBD Images for Robotic Task Learning
Christian Zimmermann, Tim Welschehold, Christian Dornhege +2
We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from c…
Deep Auxiliary Learning for Visual Localization and Odometry
Abhinav Valada, Noha Radwan, Wolfram Burgard
Localization is an indispensable component of a robot's autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action…
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
Jingwei Zhang, Lei Tai, Peng Yun +4
In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the rea…