20 citations · 36 across the 9 of their papers we have counts for
5 papers · 1 filter
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer
Elie Aljalbout, Felix Frank, Maximilian Karl +1
We study the choice of action space in robot manipulation learning and sim-to-real transfer. We define metrics that assess the performance, and examine the emerging properties in t…
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…
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…