911 citations · 2.9k across the 99 of their papers we have counts for
15 papers · 1 filter
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard +1
The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One im…
Deep Transfer Learning for Error Decoding from Non-Invasive EEG
Martin Völker, Robin T. Schirrmeister, Lukas D. J. Fiederer +2
We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learnin…
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
Lei Tai, Jingwei Zhang, Ming Liu +1
We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we ado…
Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing
Noha Radwan, Wera Winterhalter, Christian Dornhege +1
We consider the problem of developing robots that navigate like pedestrians on sidewalks through city centers for performing various tasks including delivery and surveillance. One…
From Plants to Landmarks: Time-invariant Plant Localization that uses Deep Pose Regression in Agricultural Fields
Florian Kraemer, Alexander Schaefer, Andreas Eitel +2
Agricultural robots are expected to increase yields in a sustainable way and automate precision tasks, such as weeding and plant monitoring. At the same time, they move in a contin…
Brain Responses During Robot-Error Observation
Dominik Welke, Joos Behncke, Marina Hader +6
Brain-controlled robots are a promising new type of assistive device for severely impaired persons. Little is however known about how to optimize the interaction of humans and brai…
Deep Detection of People and their Mobility Aids for a Hospital Robot
Andres Vasquez, Marina Kollmitz, Andreas Eitel +1
Robots operating in populated environments encounter many different types of people, some of whom might have an advanced need for cautious interaction, because of physical impairme…
Learning to Singulate Objects using a Push Proposal Network
Andreas Eitel, Nico Hauff, Wolfram Burgard
Learning to act in unstructured environments, such as cluttered piles of objects, poses a substantial challenge for manipulation robots. We present a novel neural network-based app…
Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills
Felix Burget, Lukas Dominique Josef Fiederer, Daniel Kuhner +8
As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic syst…
Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environments
Oier Mees, Andreas Eitel, Wolfram Burgard
Object detection is an essential task for autonomous robots operating in dynamic and changing environments. A robot should be able to detect objects in the presence of sensor noise…
Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning
Philipp Jund, Andreas Eitel, Nichola Abdo +1
To operate intelligently in domestic environments, robots require the ability to understand arbitrary spatial relations between objects and to generalize them to objects of varying…
Topometric Localization with Deep Learning
Gabriel L. Oliveira, Noha Radwan, Wolfram Burgard +1
Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost…
Deep Semantic Classification for 3D LiDAR Data
Ayush Dewan, Gabriel L. Oliveira, Wolfram Burgard
Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In th…
Neural SLAM: Learning to Explore with External Memory
Jingwei Zhang, Lei Tai, Ming Liu +2
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimi…
Metric Learning for Generalizing Spatial Relations to New Objects
Oier Mees, Nichola Abdo, Mladen Mazuran +1
Human-centered environments are rich with a wide variety of spatial relations between everyday objects. For autonomous robots to operate effectively in such environments, they shou…