Journal Paper Accepted at Neural Networks

Daniel Tanneberg, Jan Peters, Elmar Rueckert

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

accepted (Oct, 9th 2018) at Neural Networks – Elsevier with an Impact Factor of 7.197 (2017).

Conference paper accepted at VAILD 2018

Gondaliya, D. Kaushikkumar; Peters, J.; Rueckert, E. (2018). Learning to categorize bug reports with LSTM networks: An empirical study on thousands of real bug reports from a world leading software company, Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID).

Journal Paper Accepted at JMLR – Journal of Machine Learning Research.

Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

accepted (Oct, 8th 2018) at Journal of Machine Learning Research (JMLR).

1st day as Assistant Professor

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Invited Talk at the ICDL Conference, Lisbon, Portugal

Home – Background Slideshow

Title: Experience Replay and Intrinsic Motivation in Neural Motor Skill Learning Models

3 HUMANOIDS Papers Accepted

Rueckert, E.; Nakatenus, M.; Tosatto, S.; Peters, J. (2017). Learning Inverse Dynamics Models in O(n) time with LSTM networks.

Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Efficient Online Adaptation with Stochastic Recurrent Neural Networks.

Stark, S.; Peters, J.; Rueckert, E. (2017). A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries.

CoRL Paper accepted

Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals, Proceedings of the Conference on Robot Learning (CoRL).

W1 Juniorprofessorship with tenure track at University Lübeck

With February 1st, 2018 I will work as professor for robotics at the university Lübeck.

Invited Talk at University Lübeck

Title: Neural models for robot motor skill learning.

Abstract:

The challenges in understanding human motor control, in brain-machine interfaces

and anthropomorphic robotics are currently converging. Modern anthropomorphic

robots with their compliant actuators and various types of sensors (e.g., depth

and vision cameras, tactile fingertips, full-body skin, proprioception) have

reached the perceptuomotor complexity faced in human motor control and learning.

While outstanding robotic and prosthetic devices exist, current brain machine

interfaces (BMIs) and robot learning methods have not yet reached the required

autonomy and performance needed to enter daily life.

For truly autonomous robotic and prosthetic devices four major challenges have

to be addressed. These fields can be grouped into the major area of

Neurorobotics and are, (1) the decomposability of complex motor skills into

basic primitives organized in complex architectures, (2) the ability to learn

from partial observable noisy observations of inhomogeneous high-dimensional

sensor data, (3) the learning of abstract features, generalizable models and

transferable policies from human demonstrations, sparse rewards and through

active learning, and (4), accurate predictions of self-motions, object dynamics

and of humans movements for assisting and cooperating autonomous systems.

My contributions are probabilistic computational models that can be trained from

high-dimensional input streams of neural and artificial data (e.g., action

potentials, movement kinematics, joint forces, EMG signals, tactile readings).

The learned models are evaluated in human motor adaptation experiments and in

robot reaching and balancing tasks. These probabilistic models can be

co-activated and sequenced in time as movement primitives and can be modulated

by a small set of control parameters to generalize to new tasks. In neural

network implementations forward and inverse kinematic models are learned

simultaneously and used to generate movement plans in a compliant humanoid

robot. The neural models capture the correlations of the input and can forecast

self-motions or co-workers intentions as demonstrated in a recent human

adaptation experiment which showed that postural control precedes and predicts

volitional motor control.

Invited Talk at the Frankfurt Institute for Advanced Studies (FIAS), Germany

Learning to Plan through Reinforcement Learning in Spiking Neural Networks

Abstract: Movement planing is a fundamental skill that is involved in many human motor control tasks. While the hippocampus plays a central role, the functional principles underlying planning are largely unexplored. In this talk, I present a computational model for planning that is derived from theoretical principles of the probabilistic inference framework. Optimal learning rules are inferred and links to the widely used machine learning techniques expectation maximization and policy search are established. As computational model for hippocampal sweeps, we show that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. In robotic tasks, non-Gaussian hard constraints are modeled, dozens of movement plans are simulated in parallel, and forward and inverse kinematic models are learned simultaneously through interactions with the environment.