In this work, we utilize Quantum Deep Reinforcement Learning as method to learn navigation tasks for a simple, wheeled robot in three simulated environments of increasing complexity.
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.
With a Convolutional Neural Network Long Short Term Memory (CNN LSTM) on facial images an accuracy of 92% was reached on the test set.
It is being proven to what extent the algorithms can be used in the area of Reinforcement learning.
In this work, we propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD, using Bayesian Optimization, SMAC, and CMA-ES.
In this paper we introduce the Perception for Autonomous Systems (PAZ) software library.
In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors.
Deep learning models are extensively used in various safety critical applications.
We argue that for rigid-body kinematics one of the first proposed machine learning (ML) solutions to inverse kinematics -- distal teaching (DT) -- is actually good enough when combined with differentiable programming libraries and we provide an extensive evaluation and comparison to analytical and numerical solutions.
Furthermore, we will give an outlook on problems that are challenging today but might be solved by machine learning in the future and argue that classical robotics and other approaches from artificial intelligence should be integrated more with machine learning to form complete, autonomous systems.
In this work, a framework for on-line learning of robot dynamics is developed to adapt to such changes.
Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics.