A competitive mechanism for self-organized learning of sensorimotor mappings

Hemion N, Joublin F, Rohlfing K. A competitive mechanism for self-organized learning of sensorimotor mappings. In: 2011 IEEE International Conference on Development and Learning (ICDL). IEEE; 2011

The Family Transformation Due to the Assisted Reproductive Technologies

This article aims to make a brief study in the field of bioethics particularly on the influence of reproductive medicine on the family. This change is not only represented as a "crisis" of the traditional model based on marriage between men and women and its durability, but also on the proposition of the new models that contribute to this crisis. This study brings confront between these models trying also to draw conclusions about possible future scenarios and the feared crisis of identity of future generations. The aim aof this article is to demonstrate that family is still a human resourse. DOI: 10.5901/ajis.2015.v4n2p23

Building Blocks for Cognitive Robots: Embodied Simulation and Schemata in a Cognitive Architecture

Hemion N. Building Blocks for Cognitive Robots: Embodied Simulation and Schemata in a Cognitive Architecture. Bielefeld: Bielefeld University; 2013.Building robots with the ability to perform general intelligent action is a primary goal of artificial intelligence research. The traditional approach is to study and model fragments of cognition separately, with the hope that it will somehow be possible to integrate the specialist solutions into a functioning whole. However, while individual specialist systems demonstrate proficiency in their respective niche, current integrated systems remain clumsy in their performance. Recent findings in neurobiology and psychology demonstrate that many regions of the brain are involved not only in one but in a variety of cognitive tasks, suggesting that the cognitive architecture of the brain uses generic computations in a distributed network, instead of specialist computations in local modules. Designing the cognitive architecture for a robot based on these findings could lead to more capable integrated systems. In this thesis, theoretical background on the concept of embodied cognition is provided, and fundamental mechanisms of cognition are discussed that are hypothesized across theories. Based on this background, a view of how to connect elements of the different theories is proposed, providing enough detail to allow computational modeling. The view proposes a network of generic building blocks to be the central component of a cognitive architecture. Each building block learns an internal model for its inputs. Given partial inputs or cues, the building blocks can collaboratively restore missing components, providing the basis for embodied simulation, which in theories of embodied cognition is hypothesized to be a central mechanism of cognition and the basis for many cognitive functions. In simulation experiments, it is demonstrated how the building blocks can be autonomously learned by a robot from its sensorimotor experience, and that the mechanism of embodied simulation allows the robot to solve multiple tasks simultaneously. In summary, this thesis investigates how to develop cognitive robots under the paradigm of embodied cognition. It provides a description of a novel cognitive architecture and thoroughly discusses its relation to a broad body of interdisciplinary literature on embodied cognition. This thesis hence promotes the view that the cognitive system houses a network of active elements, which organize the agent's experiences and collaboratively carry out many cognitive functions. On the long run, it will be inevitable to study complete cognitive systems such as the cognitive architecture described in this thesis, instead of only studying small learning systems separately, to answer the question of how to build truly autonomous cognitive robots

Hierarchical reinforcement learning as creative problem solving

publisher: Elsevier articletitle: Hierarchical reinforcement learning as creative problem solving journaltitle: Robotics and Autonomous Systems articlelink: http://dx.doi.org/10.1016/j.robot.2016.08.021 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

The size of triangulations supporting a given link

Let T be a triangulation of S^3 containing a link L in its 1-skeleton. We give an explicit lower bound for the number of tetrahedra of T in terms of the bridge number of L. Our proof is based on the theory of almost normal surfaces.Comment: Published by Geometry and Topology at http://www.maths.warwick.ac.uk/gt/GTVol5/paper13.abs.htm

A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback

Vollmer A-L, Hemion NJ. 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. 2018;5: 77.Enabling users to teach their robots new tasks at home is a major challenge for research in personal robotics. This work presents a user study in which participants were asked to teach the robot Pepper a game of skill. The robot was equipped with a state-of-the-art skill learning method, based on dynamic movement primitives (DMPs). The only feedback participants could give was a discrete rating after each of Pepper's movement executions (“very good,” “good,” “average,” “not so good,” “not good at all”). We compare the learning performance of the robot when applying user-provided feedback with a version of the learning where an objectively determined cost via hand-coded cost function and external tracking system is applied. Our findings suggest that (a) an intuitive graphical user interface for providing discrete feedback can be used for robot learning of complex movement skills when using DMP-based optimization, making the tedious definition of a cost function obsolete; and (b) un-experienced users with no knowledge about the learning algorithm naturally tend to apply a working rating strategy, leading to similar learning performance as when using the objectively determined cost. We discuss insights about difficulties when learning from user provided feedback, and make suggestions how learning continuous movement skills from non-expert humans could be improved

Integration of sensorimotor mappings by making use of redundancies

Hemion N, Joublin F, Rohlfing K. Integration of sensorimotor mappings by making use of redundancies. In: IEEE Computational Intelligence Society, Institute of Electrical and Electronics Engineers, eds. The 2012 International Joint Conference on Neural Networks (IJCNN). Brisbane, Australia: IEEE; 2012

Algorithms for recognizing knots and 3-manifolds

This is a survey paper on algorithms for solving problems in 3-dimensional topology. In particular, it discusses Haken's approach to the recognition of the unknot, and recent variations.Comment: 17 Pages, 7 figures, to appear in Chaos, Fractals and Soliton

Interactive Robot Task Learning: Human Teaching Proficiency with Different Feedback Approaches

The deployment of versatile robot systems in diverse environments requires intuitive approaches for humans to flexibly teach them new skills. In our present work, we investigate different user feedback types to teach a real robot a new movement skill. We compare feedback as star ratings on an absolute scale for single roll-outs versus preference-based feedback for pairwise comparisons with respective optimization algorithms (i.e., a variation of co-variance matrix adaptation -evolution strategy (CMA-ES) and random optimization) to teach the robot the game of skill cup-and-ball. In an experimental investigation with users, we investigated the influence of the feedback type on the user experience of interacting with the different interfaces and the performance of the learning systems. While there is no significant difference for the subjective user experience between the conditions, there is a significant difference in learning performance. The preference-based system learned the task quicker, but this did not influence the users’ evaluation of it. In a follow-up study, we confirmed that the difference in learning performance indeed can be attributed to the human users’ performance