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20132024
most citedFlowNet: Learning Optical Flow with Convolutional Networks

604 citations · 679 across the 18 of their papers we have counts for

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5 papers · 1 filter

cs.RO20221 cited

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…

cs.RO2020

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…

cs.RO2019

Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images

Nutan Chen, Göran Westling, Benoni B. Edin +1

The study of dexterous manipulation has provided important insights in humans sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous wor…

cs.RO20162 cited

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…

cs.RO2016

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…