Ph.D. student

Machine Learning and Robotics Lab

University of Stuttgart

Github | ResearchGate | Google Scholar | PGP Key





Research

My research interests are in skill learning, robotic manipulations, and motion planning.

Specific topics that I address in my research include:

Combining optimization and reinforcement learning



As an alternative to the standard reinforcement learning formulation where all objectives are defined in a single reward function, we propose to decompose the problem into analytically known objectives, such as motion smoothness, and black-box objectives, such as trial success or reward depending on the interaction with the environment. The skill learning problem is separated into an optimal control part that improves the skill with respect to the known parts of a problem and a reinforcement learning part that learns the unknown parts by interacting with the environment.

[1] Learning Manipulation Skills from a Single Demonstration
Peter Englert and Marc Toussaint
International Journal of Robotics Research 37(1):137-154, 2018
bib | pdf | video ]


Extracting compact task representations from depth data



Kinematic morphing networks find the relation of different geometric environments and use this relation to transfer skills between the environments. We assume that the environment can be modeled as a kinematic structure and represented with a low-dimensional parametrization. A key element of this work is the usage of the concatenation property of affine transformations and the ability to convert point clouds to depth images, which allows to apply the network in an iterative manner.

[1] Kinematic Morphing Networks for Manipulation Skill Transfer
Peter Englert and Marc Toussaint
In Proceedings of the IEEE International Conference on Intelligent Robotics Systems, 2018
bib | pdf | video ]


Learning generalizable skills from demonstrations



The method presented in [1] captures the essential features of a demonstrated task in a cost function that is generalizable to various environment configurations. For this purpose, it assumes that the demonstrations are optimal with respect to an underlying constrained optimization problem. The aim of this approach is to push learning from demonstration to more complex manipulation scenarios that include the interaction with objects and therefore the realization of contacts/constraints within the motion.

[1] Inverse KKT - Learning Cost Functions of Manipulation Tasks from Demonstrations
Peter Englert, Ngo Anh Vien, and Marc Toussaint
International Journal of Robotics Research 36(13-14):1474-1488, 2017
bib | pdf ]


Learning with probabilistic models



Probabilistic models like Gaussian processes are the right choice if an uncertainty estimate of a model is important for the task. In [1], we proposed a probabilistic imitation learning formulation that learns a robot dynamics model from data. This model is used to perform a probabilistic trajectory matching to imitate the distribution of expert demonstrations. In [2], the robot only uses its tactile sensors to explore the shape of an unknown object by sliding on it. Gaussian processes are used to represent the implicit surface of the unknown object shape. The uncertainty of the model is used to guide the exploration into regions with the highest uncertainty.

[1] Probabilistic Model-based Imitation Learning
Peter Englert, Alexandros Paraschos, Marc Peter Deisenroth, and Jan Peters
Adaptive Behavior Journal 21(5):388-403, 2013
bib | pdf ]

[2] Active Learning with Query Paths for Tactile Object Shape Exploration
Danny Driess, Peter Englert, and Marc Toussaint
In Proceedings of the IEEE International Conference on Intelligent Robotics Systems, 2017
bib | pdf | video ]



Publications

Kinematic Morphing Networks for Manipulation Skill Transfer
Peter Englert and Marc Toussaint
In Proceedings of the IEEE International Conference on Intelligent Robotics Systems, 2018
bib | pdf | video ]

Learning Manipulation Skills from a Single Demonstration
Peter Englert and Marc Toussaint
International Journal of Robotics Research 37(1):137-154, 2018
bib | pdf | video ]

Inverse KKT - Learning Cost Functions of Manipulation Tasks from Demonstrations
Peter Englert, Ngo Anh Vien, and Marc Toussaint
International Journal of Robotics Research 36(13-14):1474-1488, 2017
bib | pdf ]

Active Learning with Query Paths for Tactile Object Shape Exploration
Danny Driess, Peter Englert, and Marc Toussaint
In Proceedings of the IEEE International Conference on Intelligent Robotics Systems, 2017
bib | pdf | video ]

Constrained Bayesian Optimization of Combined Interaction Force/Task Space Controllers for Manipulations
Danny Driess, Peter Englert, and Marc Toussaint
In Proceedings of the IEEE International Conference on Robotics and Automation, 2017
bib | pdf | video ]

Combined Optimization and Reinforcement Learning for Manipulations Skills
Peter Englert and Marc Toussaint
In Proceedings of Robotics: Science and Systems, 2016
bib | pdf | video ]

Policy Search in Reproducing Kernel Hilbert Space
Vien Ngo Anh, Peter Englert, and Marc Toussaint
In Proceedings of the International Joint Conference on Artificial Intelligence, 2016
bib | pdf ]

Inverse KKT - Learning Cost Functions of Manipulation Tasks from Demonstrations
Peter Englert and Marc Toussaint
In Proceedings of the International Symposium of Robotics Research, 2015
bib | pdf | video ]

Sparse Gaussian Process Regression for Compliant, Real-Time Robot Control
Jens Schreiter, Peter Englert, Duy Nguyen-Tuong, and Marc Toussaint
In Proceedings of the IEEE International Conference on Robotics and Automation, 2015
bib | pdf ]

Inverse KKT Motion Optimization: A Newton Method to Efficiently Extract Task Spaces and Cost Parameters from Demonstrations
Peter Englert and Marc Toussaint
NIPS Workshop on Autonomously Learning Robots, 2014
bib | pdf ]

Dual Execution of Optimized Contact Interaction Trajectories
Marc Toussaint, Nathan Ratliff, Jeannette Bohg, Ludovic Righetti, Peter Englert, and Stefan Schaal
In Proceedings of the IEEE International Conference on Intelligent Robotics Systems, 2014
bib | pdf ]

Reactive Phase and Task Space Adaptation for Robust Motion Execution
Peter Englert and Marc Toussaint
In Proceedings of the IEEE International Conference on Intelligent Robotics Systems, 2014
bib | pdf ]

Multi-Task Policy Search for Robotics
Marc Peter Deisenroth, Peter Englert, Jan Peters, and Dieter Fox
In Proceedings of the IEEE International Conference on Robotics and Automation, 2014
bib | pdf ]

Probabilistic Model-based Imitation Learning
Peter Englert, Alexandros Paraschos, Marc Peter Deisenroth, and Jan Peters
Adaptive Behavior Journal 21(5):388-403, 2013
bib | pdf ]

Model-based Imitation Learning by Probabilistic Trajectory Matching
Peter Englert, Alexandros Paraschos, Jan Peters, and Marc Peter Deisenroth
In Proceedings of the IEEE International Conference on Robotics and Automation, 2013
bib | pdf ]



Teaching

TA at University of Stuttgart:



Contact

Address:
University of Stuttgart
Universitaetsstr. 38
70569 Stuttgart, Germany
room: 2.240
Email:
englertpr AT gmail.com
Skype:
englert.peter