Towards Teaching a Robot to Count Objects

We present here an example of incremental learning between two computational models dealing with different modalities: a model allowing to switch spatial visual attention and a model allowing to learn the ordinal sequence of phonetical numbers. Their merging via a common reward signal allows anyway to produce a cardinal counting behaviour that can be implemented on a robot

Synthesizing SystemC Code from Delay Hybrid CSP

Delay is omnipresent in modern control systems, which can prompt oscillations and may cause deterioration of control performance, invalidate both stability and safety properties. This implies that safety or stability certificates obtained on idealized, delay-free models of systems prone to delayed coupling may be erratic, and further the incorrectness of the executable code generated from these models. However, automated methods for system verification and code generation that ought to address models of system dynamics reflecting delays have not been paid enough attention yet in the computer science community. In our previous work, on one hand, we investigated the verification of delay dynamical and hybrid systems; on the other hand, we also addressed how to synthesize SystemC code from a verified hybrid system modelled by Hybrid CSP (HCSP) without delay. In this paper, we give a first attempt to synthesize SystemC code from a verified delay hybrid system modelled by Delay HCSP (dHCSP), which is an extension of HCSP by replacing ordinary differential equations (ODEs) with delay differential equations (DDEs). We implement a tool to support the automatic translation from dHCSP to SystemC

From Holistic to Discrete Speech Sounds: The Blind Snow-Flake Maker Hypothesis

Sound is a medium used by humans to carry information. The existence of this kind of medium is a pre-requisite for language. It is organized into a code, called speech, which provides a repertoire of forms that is shared in each language community. This code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is characterized by several properties: speech is digital and compositional (vocalizations are made of units re-used systematically in other syllables); phoneme inventories have precise regularities as well as great diversity in human languages; all the speakers of a language community categorize sounds in the same manner, but each language has its own system of categorization, possibly very different from every other. How can a speech code with these properties form? These are the questions we will approach in the paper. We will study them using the method of the artificial. We will build a society of artificial agents, and study what mechanisms may provide answers. This will not prove directly what mechanisms were used for humans, but rather give ideas about what kind of mechanism may have been used. This allows us to shape the search space of possible answers, in particular by showing what is sufficient and what is not necessary. The mechanism we present is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices allows a population of agents to build a speech code that has the properties mentioned above. The originality is that it pre-supposes neither a functional pressure for communication, nor the ability to have coordinated social interactions (they do not play language or imitation games). It relies on the self-organizing properties of a generic coupling between perception and production both within agents, and on the interactions between agents

Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.Comment: Published at ACC 201

From Analogue to Digital Vocalizations

Sound is a medium used by humans to carry information. The existence of this kind of medium is a pre-requisite for language. It is organized into a code, called speech, which provides a repertoire of forms that is shared in each language community. This code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is characterized by several properties: speech is digital and compositional (vocalizations are made of units re-used systematically in other syllables); phoneme inventories have precise regularities as well as great diversity in human languages; all the speakers of a language community categorize sounds in the same manner, but each language has its own system of categorization, possibly very different from every other. How can a speech code with these properties form? These are the questions we will approach in the paper. We will study them using the method of the artificial. We will build a society of artificial agents, and study what mechanisms may provide answers. This will not prove directly what mechanisms were used for humans, but rather give ideas about what kind of mechanism may have been used. This allows us to shape the search space of possible answers, in particular by showing what is sufficient and what is not necessary. The mechanism we present is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices allows a population of agents to build a speech code that has the properties mentioned above. The originality is that it pre-supposes neither a functional pressure for communication, nor the ability to have coordinated social interactions (they do not play language or imitation games). It relies on the self-organizing properties of a generic coupling between perception and production both within agents, and on the interactions between agents

Characteristics of Precession Electron Diffraction Intensities from Dynamical Simulations

Precession Electron Diffraction (PED) offers a number of advantages for crystal structure analysis and solving unknown structures using electron diffraction. The current article uses many-beam simulations of PED intensities, in combination with model structures, to arrive at a better understanding of how PED differs from standard unprecessed electron diffraction. It is shown that precession reduces the chaotic oscillatory behavior of electron diffraction intensities as a function of thickness. An additional characteristic of PED which is revealed by simulations is reduced sensitivity to structure factor phases. This is shown to be a general feature of dynami-cal intensities collected under conditions in which patterns with multiple incident beam orienta-tions are averaged together. A new and significantly faster method is demonstrated for dynami-cal calculations of PED intensities, based on using information contained in off-central columns of the scattering matrix.Comment: 20 pages, 7 Figure