Learning movement primitive libraries through probabilistic segmentation von Lioutikov, Rudolf, Neumann, Gerhard, Maeda, Guilherme, Peters, Jan

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Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks von Maeda, Guilherme J., Neumann, Gerhard, Ewerton, Marco, Lioutikov, Rudolf, Kroemer, Oliver, Peters, Jan

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Phase estimation for fast action recognition and trajectory generation in human–robot collaboration von Maeda, Guilherme, Ewerton, Marco, Neumann, Gerhard, Lioutikov, Rudolf, Peters, Jan

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Directly Printable Flexible Strain Sensors for Bending and Contact Feedback of Soft Actuators von Elgeneidy, Khaled, Neumann, Gerhard, Jackson, Michael, Lohse, Niels

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Deep Reinforcement Learning for Attacking Wireless Sensor Networks von Parras, Juan, Hüttenrauch, Maximilian, Zazo, Santiago, Neumann, Gerhard

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Sim-to-Real Quadrotor Landing via Sequential Deep Q-Networks and Domain Randomization von Polvara, Riccardo, Patacchiola, Massimiliano, Hanheide, Marc, Neumann, Gerhard

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Guiding Trajectory Optimization by Demonstrated Distributions von Osa, Takayuki, Ghalamzan Esfahani, Amir M., Stolkin, Rustam, Lioutikov, Rudolf, Peters, Jan, Neumann, Gerhard

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Generalization of human grasping for multi-fingered robot hands von Ben Amor, Heni, Kroemer, O., Hillenbrand, U., Neumann, G., Peters, J.

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Using probabilistic movement primitives in robotics von Paraschos, Alexandros, Daniel, Christian, Peters, Jan, Neumann, Gerhard

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Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation von Ashith Shyam, R B, Hao, Zhou, Montanaro, Umberto, Dixit, Shilp, Rathinam, Arunkumar, Gao, Yang, Neumann, Gerhard, Fallah, Saber

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Biologically inspired kinematic synergies enable linear balance control of a humanoid robot von Hauser, Helmut, Neumann, Gerhard, Ijspeert, Auke J., Maass, Wolfgang

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Learned graphical models for probabilistic planning provide a new class of movement primitives von Rückert, Elmar A, Neumann, Gerhard, Toussaint, Marc, Maass, Wolfgang

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Adaptation and Robust Learning of Probabilistic Movement Primitives von Gomez-Gonzalez, Sebastian, Neumann, Gerhard, Scholkopf, Bernhard, Peters, Jan

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Probabilistic inference for determining options in reinforcement learning von Daniel, Christian, van Hoof, Herke, Peters, Jan, Neumann, Gerhard

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Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes von Würth, Tobias, Freymuth, Niklas, Zimmerling, Clemens, Neumann, Gerhard, Kärger, Luise

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A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping von Abi-Farraj, Firas, Pacchierotti, Claudio, Arenz, Oleg, Neumann, Gerhard, Giordano, Paolo Robuffo

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Model-based contextual policy search for data-efficient generalization of robot skills von Kupcsik, Andras, Deisenroth, Marc Peter, Peters, Jan, Loh, Ai Poh, Vadakkepat, Prahlad, Neumann, Gerhard

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Learning Kalman Network: A deep monocular visual odometry for on-road driving von Zhao, Cheng, Sun, Li, Yan, Zhi, Neumann, Gerhard, Duckett, Tom, Stolkin, Rustam

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Compatible natural gradient policy search von Pajarinen, Joni, Thai, Hong Linh, Akrour, Riad, Peters, Jan, Neumann, Gerhard

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The kernel Kalman rule: Efficient nonparametric inference by recursive least-squares and subspace projections von Gebhardt, Gregor H. W., Kupcsik, Andras, Neumann, Gerhard

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