Rapid learning of humanoid body schemas with kinematic Bezier maps

Trabajo presentado al 9th IEEE-RAS celebrado en París del 7 al 10 de diciembre de 2009.This paper addresses the problem of hand-eye coordination and, more specifically, tool-eye recalibration of humanoid robots. Inspired by results from neuroscience, a novel method to learn the forward kinematics model as part of the body schema of humanoid robots is presented. By making extensive use of techniques borrowed from the field of computer-aided geometry, the proposed Kinematic Be ́zier Maps (KB-Maps) permit reducing this complex problem to a linearly-solvable, although high-dimensional, one. Therefore, in the absence of noise, an exact kinematic model is obtained. This leads to rapid learning which, unlike in other approaches, is combined with good extrapolation capabilities. These promising theoretical advantages have been validated through simulation, and the applicability of the method to real hardware has been demonstrated through experiments on the humanoid robot ARMAR-IIIa.This work was supported by projects: 'Perception, action & cognition through learning of object-action complexes.' (4915), 'Analysis and motion planning of complex robotic systems' (4802), 'Grup de recerca consolidat - Grup de Robòtica' (4810). The work described in this paper was partially conducted within the EU Cognitive Systems projects GRASP (FP7-215821) and PACO-PLUS (FP6-027657) funded by the European Commission. The authors acknowledge support from the Generalitat de Catalunya under the consolidated Robotics group, and from the Spanish Ministry of Science and Education, under the project DPI2007-60858Peer Reviewe

Object-action complexes: Grounded abstractions of sensory-motor processes

El pdf del artículo es la versión pre-print.-- et al.This paper formalises Object-Action Complexes (OACs) as a basis for symbolic representations of sensory-motor experience and behaviours. OACs are designed to capture the interaction between objects and associated actions in artificial cognitive systems. This paper gives a formal definition of OACs, provides examples of their use for autonomous cognitive robots, and enumerates a number of critical learning problems in terms of OACs.The research leading to these results received funding from the European Union through the Sixth Framework PACO-PLUS project (IST-FP6-IP-027657) and the Seventh Framework XPERIENCE project (FP7/2007-2013, Grant No. 270273).Peer Reviewe

General robot kinematics decomposition without intermediate markers

The calibration of serial manipulators with high numbers of degrees of freedom by means of machine learning is a complex and time-consuming task. With the help of a simple strategy, this complexity can be drastically reduced and the speed of the learning procedure can be increased: When the robot is virtually divided into shorter kinematic chains, these subchains can be learned separately and, hence, much more efficiently than the complete kinematics. Such decompositions, however, require either the possibility to capture the poses of all end- effectors of all subchains at the same time, or they are limited to robots that fulfill special constraints. In this work, an alternative decomposition is presented that does not suffer from these limitations. An offline training algorithm is provided in which the composite subchains are learned sequentially with dedicated movements. A second training scheme is provided to train composite chains simultaneously and online. Both schemes can be used together with many machine learning algorithms. In the simulations, an algorithm using Parameterized Self-Organizing Maps (PSOM) modified for online learning and Gaussian Mixture Models (GMM) were chosen to show the correctness of the approach. The experimental results show that, using a two-fold decomposition, the number of samples required to reach a given precision is reduced to twice the square root of the original number.The work described in this paper was partially conducted within the EU Cognitive Systems projects Xperience (FP-7-270273) and GARNICS (FP-7-247947) funded by the European Commission, and the Generalitat de Catalunya through the Robotics group (SGR2009-00155). V. Ruiz de Angulo acknowledges support from Spanish Ministry of Science and Education, under the project DPI2010-18449. C. Torras acknowledges support from the Consolider project MIPRCV (CSD2007-00018).Peer Reviewe

Kinematic Bézier maps

16 páginas, 18 figuras.The kinematics of a robot with many degrees of freedom is a very complex function. Learning this function for a large workspace with a good precision requires a huge number of training samples, i.e., robot movements. In this work, we introduce the Kinematic Bézier Map (KB-Map), a parametrizable model without the generality of other systems, but whose structure readily incorporates some of the geometric constraints of a kinematic function. In this way, the number of training samples required is drastically reduced. Moreover, the simplicity of the model reduces learning to solving a linear least squares problem. Systematic experiments have been carried out showing the excellent interpolation and extrapolation capabilities of KB-Maps and their relatively low sensitivity to noise.The work described in this paper was partially conducted within the EU Cognitive Systems projects IntellAct (FP-7-269959) and GRASP (FP-7-215821) funded by the European Commission. V. Ruiz de Angulo and C. Torras acknowledge support from the Generalitat de Catalunya under the consolidated Robotics group, and from the Spanish Ministry of Science and Education, under the projects DPI2010-18449 and CSD2007-00018, respecively.Peer reviewe

A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation

Developing neuro-inspired computing paradigms that mimic nervous system function is an emerging field of research that fosters our model understanding of the biological system and targets technical applications in artificial systems. The computational power of simulated brain circuits makes them a very promising tool for the development for brain-controlled robots. Early phases of robotic controllers development make extensive use of simulators as they are easy, fast and cheap tools. In order to develop robotics controllers that encompass brain models, a tool that include both neural simulation and physics simulation is missing. Such a tool would require the capability of orchestrating and synchronizing both simulations as well as managing the exchange of data between them. The Neurorobotics Platform (NRP) aims at filling this gap through an integrated software toolkit enabling an experimenter to design and execute a virtual experiment with a simulated robot using customized brain models. As a use case for the NRP, the iCub robot has been integrated into the platform and connected to a spiking neural network. In particular, experiments of visual tracking have been conducted in order to demonstrate the potentiality of such a platform