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ORCIDMaximilian Karl
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2020 – today
- 2024
[j3]Elie Aljalbout
, Felix Frank, Maximilian Karl, Patrick van der Smagt:
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer. IEEE Robotics Autom. Lett. 9(6): 5895-5902 (2024)- [c12]Nikolas J. Wilhelm, Sami Haddadin, Rainer Burgkart, Patrick van der Smagt, Maximilian Karl:
Accurate Kinematic Modeling using Autoencoders on Differentiable Joints. ICRA 2024: 7122-7128 - [c11]Nikolas J. Wilhelm, Claudio Glowalla, Sami Haddadin, Julian Schote, Hannes Höppner, Patrick van der Smagt, Maximilian Karl, Rainer Burgkart:
Design and Implementation of a Robotic Testbench for Analyzing Pincer Grip Execution in Human Specimen Hands. ICRA 2024: 18465-18471 - [i13]Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl:
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models. CoRR abs/2404.18896 (2024) - [i12]Elie Aljalbout, Nikolaos Sotirakis, Patrick van der Smagt, Maximilian Karl, Nutan Chen:
LIMT: Language-Informed Multi-Task Visual World Models. CoRR abs/2407.13466 (2024) - 2023
- [j2]Nutan Chen, Walterio W. Mayol-Cuevas, Maximilian Karl, Elie Aljalbout, Andy Zeng, Aurelio Cortese, Wolfram Burgard, Herke van Hoof:
Editorial: Language, affordance and physics in robot cognition and intelligent systems. Frontiers Robotics AI 10 (2023) - [c10]Chukwuka Victor Obionwu, Maximilian Karl, David Broneske, Anja Hawlitschek, Paul Blockhaus, Gunter Saake:
A Strategy for Structuring Teams Collaboration in University Course Projects. ICSBT 2023: 32-42 - [c9]Elie Aljalbout, Maximilian Karl, Patrick van der Smagt:
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces. L4DC 2023: 1152-1166 - [c8]Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl:
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models. NeurIPS 2023 - [i11]Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl:
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models. CoRR abs/2312.02019 (2023) - [i10]Elie Aljalbout, Felix Frank, Maximilian Karl, Patrick van der Smagt:
On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer. CoRR abs/2312.03673 (2023) - 2022
- [c7]Maximilian Karl, Raphael Zender:
Automated feedback on teamwork in programming courses. DELFI 2022: 221-222 - [i9]Elie Aljalbout, Maximilian Karl, Patrick van der Smagt:
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces. CoRR abs/2211.15824 (2022) - 2020
- [b1]Maximilian Karl:
Unsupervised Control: Efficient Inference for Time Series Modelling and Intrinsic Motivation. Technical University of Munich, Germany, 2020 - [j1]Maximilian Karl, Lasse H. E. Thamdrup, Jukka Rantanen, Anja Boisen, Thomas Rades:
Temperature-Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition. Sensors 20(4): 1019 (2020) - [i8]Philip Becker-Ehmck, Maximilian Karl, Jan Peters, Patrick van der Smagt:
Learning to Fly via Deep Model-Based Reinforcement Learning. CoRR abs/2003.08876 (2020)
2010 – 2019
- 2019
- [c6]Maximilian Karl, Philip Becker-Ehmck, Maximilian Soelch, Djalel Benbouzid, Patrick van der Smagt, Justin Bayer:
Unsupervised Real-Time Control Through Variational Empowerment. ISRR 2019: 158-173 - [i7]Neha Das, Maximilian Karl, Philip Becker-Ehmck, Patrick van der Smagt:
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations. CoRR abs/1911.00756 (2019) - 2017
- [c5]Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. ICLR (Poster) 2017 - 2016
- [c4]Nutan Chen, Maximilian Karl, Patrick van der Smagt:
Dynamic movement primitives in latent space of time-dependent variational autoencoders. Humanoids 2016: 629-636 - [c3]Herke van Hoof, Nutan Chen, Maximilian Karl, Patrick van der Smagt, Jan Peters:
Stable reinforcement learning with autoencoders for tactile and visual data. IROS 2016: 3928-3934 - [i6]Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. CoRR abs/1605.06432 (2016) - [i5]Maximilian Karl, Artur Lohrer, Dhananjay Shah, Frederik Diehl, Max Fiedler, Saahil Ognawala, Justin Bayer, Patrick van der Smagt:
ML-based tactile sensor calibration: A universal approach. CoRR abs/1606.06588 (2016) - [i4]Maximilian Karl, Justin Bayer, Patrick van der Smagt:
Unsupervised preprocessing for Tactile Data. CoRR abs/1606.07312 (2016) - 2015
- [i3]Justin Bayer, Maximilian Karl, Daniela Korhammer, Patrick van der Smagt:
Fast Adaptive Weight Noise. CoRR abs/1507.05331 (2015) - [i2]Maximilian Karl, Justin Bayer, Patrick van der Smagt:
Efficient Empowerment. CoRR abs/1509.08455 (2015) - 2014
- [c2]Martin Eder, Maximilian Karl, Alois C. Knoll, Stefan Riesner:
Continuum worm-like robotic mechanism with decentral control architecture. CASE 2014: 866-871 - [i1]Maximilian Karl, Christian Osendorfer:
Improving approximate RPCA with a k-sparsity prior. CoRR abs/1412.8291 (2014) - 2013
- [c1]Martin Eder, Maximilian Karl, Felix Schultheiss, Johannes Schürmann, Alois C. Knoll, Stefan Riesner:
Design of an inherently safe worm-like robot. SSRR 2013: 1-6
Coauthor Index
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