194 citations · 1.5k across the 123 of their papers we have counts for
23 papers · 1 filter
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations
Malte Mosbach, Kara Moraw, Sven Behnke
Dexterous manipulation with anthropomorphic robot hands remains a challenging problem in robotics because of the high-dimensional state and action spaces and complex contacts. Neve…
Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels
Arul Selvam Periyasamy, Luis Denninger, Sven Behnke
Object pose estimation is a necessary prerequisite for autonomous robotic manipulation, but the presence of symmetry increases the complexity of the pose estimation task. Existing…
State Estimation for Hybrid Locomotion of Driving-Stepping Quadrupeds
Mojtaba Hosseini, Diego Rodriguez, Sven Behnke
Fast and versatile locomotion can be achieved with wheeled quadruped robots that drive quickly on flat terrain, but are also able to overcome challenging terrain by adapting their…
Object-level 3D Semantic Mapping using a Network of Smart Edge Sensors
Julian Hau, Simon Bultmann, Sven Behnke
Autonomous robots that interact with their environment require a detailed semantic scene model. For this, volumetric semantic maps are frequently used. The scene understanding can…
Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies
Malte Mosbach, Sven Behnke
Grasping objects of different shapes and sizes - a foundational, effortless skill for humans - remains a challenging task in robotics. Although model-based approaches can predict s…
PermutoSDF: Fast Multi-View Reconstruction with Implicit Surfaces using Permutohedral Lattices
Radu Alexandru Rosu, Sven Behnke
Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast t…
Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation
Simon Bultmann, Jan Quenzel, Sven Behnke
Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors have tremendous potential for fast autonomous or remote-controlled semantic scene analysis, e.g., for d…
Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections
Bastian Pätzold, Simon Bultmann, Sven Behnke
Calibration of multi-camera systems, i.e. determining the relative poses between the cameras, is a prerequisite for many tasks in computer vision and robotics. Camera calibration i…
Predictive Angular Potential Field-based Obstacle Avoidance for Dynamic UAV Flights
Daniel Schleich, Sven Behnke
In recent years, unmanned aerial vehicles (UAVs) are used for numerous inspection and video capture tasks. Manually controlling UAVs in the vicinity of obstacles is challenging, ho…
Direct Centroidal Control for Balanced Humanoid Locomotion
Grzegorz Ficht, Sven Behnke
We present an integrated approach to locomotion and balancing of humanoid robots based on direct centroidal control. Our method uses a five-mass description of a humanoid. It gener…
Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images
Radu Alexandru Rosu, Shunsuke Saito, Ziyan Wang +3
We present Neural Strands, a novel learning framework for modeling accurate hair geometry and appearance from multi-view image inputs. The learned hair model can be rendered in rea…
Predicting Physical Object Properties from Video
Martin Link, Max Schwarz, Sven Behnke
We present a novel approach to estimating physical properties of objects from video. Our approach consists of a physics engine and a correction estimator. Starting from the initial…
ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Poses
Arul Selvam Periyasamy, Catherine Capellen, Max Schwarz +1
Object pose estimation is a key perceptual capability in robotics. We propose a fully-convolutional extension of the PoseCNN method, which densely predicts object translations and…
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression
Arash Amini, Arul Selvam Periyasamy, Sven Behnke
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network…
Two-step Planning of Dynamic UAV Trajectories using Iterative -Spaces
Sebastian Schräder, Daniel Schleich, Sven Behnke
UAV trajectory planning is often done in a two-step approach, where a low-dimensional path is refined to a dynamic trajectory. The resulting trajectories are only locally optimal,…
3D Semantic Scene Perception using Distributed Smart Edge Sensors
Simon Bultmann, Sven Behnke
We present a system for 3D semantic scene perception consisting of a network of distributed smart edge sensors. The sensor nodes are based on an embedded CNN inference accelerator…
Abstract Flow for Temporal Semantic Segmentation on the Permutohedral Lattice
Peer Schütt, Radu Alexandru Rosu, Sven Behnke
Semantic segmentation is a core ability required by autonomous agents, as being able to distinguish which parts of the scene belong to which object class is crucial for navigation…
Real-Robot Deep Reinforcement Learning: Improving Trajectory Tracking of Flexible-Joint Manipulator with Reference Correction
Dmytro Pavlichenko, Sven Behnke
Flexible-joint manipulators are governed by complex nonlinear dynamics, defining a challenging control problem. In this work, we propose an approach to learn an outer-loop joint tr…
Synthetic-to-Real Domain Adaptation using Contrastive Unpaired Translation
Benedikt T. Imbusch, Max Schwarz, Sven Behnke
The usefulness of deep learning models in robotics is largely dependent on the availability of training data. Manual annotation of training data is often infeasible. Synthetic data…
MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networks
Angel Villar-Corrales, Ani Karapetyan, Andreas Boltres +1
Autonomous systems not only need to understand their current environment, but should also be able to predict future actions conditioned on past states, for instance based on captur…
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis
Michael Gref, Nike Matthiesen, Sreenivasa Hikkal Venugopala +5
For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize a…
Human and Automatic Speech Recognition Performance on German Oral History Interviews
Michael Gref, Nike Matthiesen, Christoph Schmidt +2
Automatic speech recognition systems have accomplished remarkable improvements in transcription accuracy in recent years. On some domains, models now achieve near-human performance…
Target Chase, Wall Building, and Fire Fighting: Autonomous UAVs of Team NimbRo at MBZIRC 2020
Marius Beul, Max Schwarz, Jan Quenzel +11
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 posed diverse challenges for unmanned aerial vehicles (UAVs). We present our four tailored UAVs, specifically d…