Nikolas Hemion

Nikolas Hemion

Robotics and A.I. Researcher


Nikolas Hemion
SoftBank Robotics Europe
43 Rue du Colonel Pierre Avia
75015 Paris
(Google maps)


Senior Researcher, director of the AI Lab at SoftBank Robotics Europe
Co-director of the H2020 APRIL project (Marie Skłodowska-Curie ITN-EID)


Fundamental Research in Machine Learning for Robotics

The Artificial Intelligence group (AI Lab) is the fundamental research unit of SoftBank Robotics Europe. We focus on developmental robotics, with the goal to make progress in the understanding and modeling of the mechanisms of development and learning in robots.

Learning from Demonstration

Enabling users to teach their robots new tasks at home is a major challenge for research in personal robotics. Dynamic Movement Primitives provide a promising basis for building a learning system that could be employed for such a task.
These two videos show how Pepper can be taught new skills in relatively short time. We use a "learning by demonstration" procedure, where a movement is first demonstrated to the robot by guiding its arm.
From there, Pepper has to improve its performance through trial-and-error learning. Even when the initial demonstration is not a successful task execution, Pepper can still learn the skill.
The movement is optimized using simple evolutionary search (CMA-ES). Our implementation uses the freely available software library dmpbbo.
See the Frontiers In Robotics And AI paper for more details.
Ball-in-a-cup (bilboquet):
Velcro dart:

Gaining Control over the own Body through Trial-and-Error Learning

In this work done by my PhD student Pontus Loviken, the robot Nao learns to take control of its own body. It is given no information about the structure of its body, and has to figure everything out strictly through trial-and-error learning. Its goal is to be learn how to roll its body (one of the very first locomotion behaviors infants acquire in their development).
In contrast to standard Reinforcement Learning techniques to learn this type of robot behavior, the learning system in this case efficiently accumulates knowledge (in the form of a model) about the way its own body behaves in the environment. This is beneficial, as it allows the robot to exploit the gained knowledge to produce a multitude of behaviors, whereas Reinforcement Learning only optimizes a single behavior.
See the IROS paper for more details.
A video of Nao learning:



Anna-Lisa Vollmer, and Nikolas J. Hemion. "A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback." Frontiers in Robotics and AI, vol 5, article 77. 2018.
Pontus Loviken, Nikolas J. Hemion, Alban Laflaquière, Michael Spranger, and Angelo Cangelosi. "Online Learning of Body Orientation Control on a Humanoid Robot using Finite Element Goal Babbling." IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2018.
Pontus Loviken, and Nikolas J. Hemion. "Online-Learning and Planning in High Dimensions with Finite Element Goal Babbling." Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 2017.
Nikolas J. Hemion. "Discovering Latent States for Model Learning: Applying Sensorimotor Contingencies Theory and Predictive Processing to Model Context." ArXiv paper. 2016.
Nikolas J. Hemion. "Context Discovery for Model Learning in Partially Observable Environments." Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 2016.
Thomas R. Colin, Tony Belpaeme, Angelo Cangelosi, and Nikolas J. Hemion. "Hierarchical Reinforcement Learning as Creative Problem Solving." Robotics and Autonomous Systems, vol 86, pp 196–206. 2016.
Alban Laflaquière, and Nikolas J. Hemion. "Grounding object perception in a naive agent's sensorimotor experience." Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). 2015.
Alban Laflaquière, Nikolas J. Hemion, Michaël Garcia Ortiz, and Jean-Christophe Baillie. "Grounding Perception: A Developmental Approach to Sensorimotor Contingencies" IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2015.
Nikolas J. Hemion. "On learning and representations in cognitive architectures for HRI" Workshop on Cognitive Architectures for Human-Robot Interaction, ACM/IEEE International Conference on Human-Robot Interaction. 2014.
Nikolas J. Hemion, "Building Blocks for Cognitive Robots: Embodied Simulation and Schemata in a Cognitive Architecture." Ph.D. dissertation, Technische Fakultät, Universität Bielefeld. 2013.
Nikolas J. Hemion, Frank Joublin, and Katharina J. Rohlfing. "Integration of sensorimotor mappings by making use of redundancies." IEEE International Joint Conference on Neural Networks (IJCNN). 2012.
Nikolas J. Hemion, Frank Joublin, and Katharina J. Rohlfing. "A competitive mechanism for self-organized learning of sensorimotor mappings." IEEE International Conference on Development and Learning (ICDL). 2011.

Brief Bio

Dr. Nikolas Hemion received his BS/MS ("Dipl.-Inform.") in Informatics in the Natural Sciences from Bielefeld University, Germany, and received his his Ph.D. degree ("Dr. Ing.") in Intelligent Systems in 2013 from the Research Institute for Cognition and Robotics, Bielefeld University, Germany. During his doctoral studies, he collaborated with the Honda Research Institute Europe, and visited the Centre for Robotics and Neural Systems, Plymouth University, UK. Subsequently he joined the AI Lab at SoftBank Robotics Europe as senior researcher, and was appointed as director of the AI Lab in 2015. Nikolas is also co-director of the H2020 Marie Skłodowska-Curie Initial Training Network APRIL (Applications of Personal Robotics for Interaction and Learning). His research interests lie in cognitive architecture and model learning for cognitive robotics, and self-organized learning of sensorimotor representations.