11 citations · 15 across the 4 of their papers we have counts for
6 papers
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
Elie Aljalbout, Maximilian Karl, Patrick van der Smagt
Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts. A typical example of such real-world tasks is dual-arm man…
Learning to Fly via Deep Model-Based Reinforcement Learning
Philip Becker-Ehmck, Maximilian Karl, Jan Peters +1
Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved li…
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
Neha Das, Maximilian Karl, Philip Becker-Ehmck +1
Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision maki…
Unsupervised Real-Time Control through Variational Empowerment
Maximilian Karl, Maximilian Soelch, Philip Becker-Ehmck +3
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control.…
Unsupervised preprocessing for Tactile Data
Maximilian Karl, Justin Bayer, Patrick van der Smagt
Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors. We make use of an…
ML-based tactile sensor calibration: A universal approach
Maximilian Karl, Artur Lohrer, Dhananjay Shah +5
We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli. The question of interest is to which degree both…