32 citations · 124 across the 21 of their papers we have counts for
24 papers
CoBaIR: A Python Library for Context-Based Intention Recognition in Human-Robot-Interaction
Adrian Lubitz, Lisa Gutzeit, Frank Kirchner
Human-Robot Interaction (HRI) becomes more and more important in a world where robots integrate fast in all aspects of our lives but HRI applications depend massively on the utiliz…
Towards Continuous Time Finite Horizon LQR Control in SE(3)
Shivesh Kumar, Andreas Mueller, Patrick Wensing +1
The control of free-floating robots requires dealing with several challenges. The motion of such robots evolves on a continuous manifold described by the Special Euclidean Group of…
Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems
Amr Gomaa, Bilal Mahdy, Niko Kleer +3
Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e.g., through sensor fusion and natural language understanding). Ho…
End-to-End Reinforcement Learning for Torque Based Variable Height Hopping
Raghav Soni, Daniel Harnack, Hannah Isermann +3
Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has…
EEG and EMG dataset for the detection of errors introduced by an active orthosis device
Niklas Kueper, Kartik Chari, Judith Bütefür +6
This paper presents a dataset containing recordings of the electroencephalogram (EEG) and the electromyogram (EMG) from eight subjects who were assisted in moving their right arm b…
AcroMonk: A Minimalist Underactuated Brachiating Robot
Mahdi Javadi, Daniel Harnack, Paula Stocco +4
Brachiation is a dynamic, coordinated swinging maneuver of body and arms used by monkeys and apes to move between branches. As a unique underactuated mode of locomotion, it is inte…
Multi-stage warm started optimal motion planning for over-actuated mobile platforms
G. J. Paz-Delgado, C. J. Pérez-del-Pulgar, M. Azkarate +2
This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinemat…
The influence of labeling techniques in classifying human manipulation movement of different speed
Sadique Adnan Siddiqui, Lisa Gutzeit, Frank Kirchner
In this work, we investigate the influence of labeling methods on the classification of human movements on data recorded using a marker-based motion capture system. The dataset is…
Quantum Deep Reinforcement Learning for Robot Navigation Tasks
Hans Hohenfeld, Dirk Heimann, Felix Wiebe +1
We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a simple, wheeled robot in simulated environments of increasing complexity. For this, we train p…
The VVAD-LRS3 Dataset for Visual Voice Activity Detection
Adrian Lubitz, Matias Valdenegro-Toro, Frank Kirchner
Robots are becoming everyday devices, increasing their interaction with humans. To make human-machine interaction more natural, cognitive features like Visual Voice Activity Detect…
Grasp stability prediction with time series data based on STFT and LSTM
Tao Wang, Frank Kirchner
With an increasing demand for robots, robotic grasping will has a more important role in future applications. This paper takes grasp stability prediction as the key technology for…
Design, analysis and control of the series-parallel hybrid RH5 humanoid robot
Julian Esser, Shivesh Kumar, Heiner Peters +5
Last decades of humanoid research has shown that humanoids developed for high dynamic performance require a stiff structure and optimal distribution of mass--inertial properties. H…
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?
Matthias Rosynski, Frank Kirchner, Matias Valdenegro-Toro
Deep Reinforcement Learning (DRL) connects the classic Reinforcement Learning algorithms with Deep Neural Networks. A problem in DRL is that CNNs are black-boxes and it is hard to…
Black-Box Optimization of Object Detector Scales
Mohandass Muthuraja, Octavio Arriaga, Paul Plöger +2
Object detectors have improved considerably in the last years by using advanced CNN architectures. However, many detector hyper-parameters are generally manually tuned, or they are…
Perception for Autonomous Systems (PAZ)
Octavio Arriaga, Matias Valdenegro-Toro, Mohandass Muthuraja +2
In this paper we introduce the Perception for Autonomous Systems (PAZ) software library. PAZ is a hierarchical perception library that allow users to manipulate multiple levels of…
Learning Context-Adaptive Task Constraints for Robotic Manipulation
Dennis Mronga, Frank Kirchner
Constraint-based control approaches offer a flexible way to specify robotic manipulation tasks and execute them on robots with many degrees of freedom. However, the specification o…
Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds
Swaroop Bhandary K, Nico Hochgeschwender, Paul Plöger +2
Deep learning models are extensively used in various safety critical applications. Hence these models along with being accurate need to be highly reliable. One way of achieving thi…
A Development Cycle for Automated Self-Exploration of Robot Behaviors
Thomas M. Roehr, Daniel Harnack, Hendrik Wöhrle +7
In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors. With Q-Rock, we suggest a novel, integrative approac…
Combinatorics of a Discrete Trajectory Space for Robot Motion Planning
Felix Wiebe, Shivesh Kumar, Daniel Harnack +3
Motion planning is a difficult problem in robot control. The complexity of the problem is directly related to the dimension of the robot's configuration space. While in many theore…
Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms
Tim von Oehsen, Alexander Fabisch, Shivesh Kumar +1
Several publications are concerned with learning inverse kinematics, however, their evaluation is often limited and none of the proposed methods is of practical relevance for rigid…
A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives
Alexander Fabisch, Christoph Petzoldt, Marc Otto +1
Recent success of machine learning in many domains has been overwhelming, which often leads to false expectations regarding the capabilities of behavior learning in robotics. In th…
A Framework for On-line Learning of Underwater Vehicles Dynamic Models
Bilal Wehbe, Marc Hildebrandt, Frank Kirchner
Learning the dynamics of robots from data can help achieve more accurate tracking controllers, or aid their navigation algorithms. However, when the actual dynamics of the robots c…
Learning of Multi-Context Models for Autonomous Underwater Vehicles
Bilal Wehbe, Octavio Arriaga, Mario Michael Krell +1
Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics. This work addresses the problem of identifying mu…
Dynamic Motion Modelling for Legged Robots
Mark Edgington, Yohannes Kassahun, Frank Kirchner
An accurate motion model is an important component in modern-day robotic systems, but building such a model for a complex system often requires an appreciable amount of manual effo…