TOWARDS THE GROUNDING OF ABSTRACT CATEGORIES IN COGNITIVE ROBOTS

The grounding of language in humanoid robots is a fundamental problem, especially in social scenarios which involve the interaction of robots with human beings. Indeed, natural language represents the most natural interface for humans to interact and exchange information about concrete entities like KNIFE, HAMMER and abstract concepts such as MAKE, USE. This research domain is very important not only for the advances that it can produce in the design of human-robot communication systems, but also for the implication that it can have on cognitive science. Abstract words are used in daily conversations among people to describe events and situations that occur in the environment. Many scholars have suggested that the distinction between concrete and abstract words is a continuum according to which all entities can be varied in their level of abstractness. The work presented herein aimed to ground abstract concepts, similarly to concrete ones, in perception and action systems. This permitted to investigate how different behavioural and cognitive capabilities can be integrated in a humanoid robot in order to bootstrap the development of higher-order skills such as the acquisition of abstract words. To this end, three neuro-robotics models were implemented. The first neuro-robotics experiment consisted in training a humanoid robot to perform a set of motor primitives (e.g. PUSH, PULL, etc.) that hierarchically combined led to the acquisition of higher-order words (e.g. ACCEPT, REJECT). The implementation of this model, based on a feed-forward artificial neural networks, permitted the assessment of the training methodology adopted for the grounding of language in humanoid robots. In the second experiment, the architecture used for carrying out the first study was reimplemented employing recurrent artificial neural networks that enabled the temporal specification of the action primitives to be executed by the robot. This permitted to increase the combinations of actions that can be taught to the robot for the generation of more complex movements. For the third experiment, a model based on recurrent neural networks that integrated multi-modal inputs (i.e. language, vision and proprioception) was implemented for the grounding of abstract action words (e.g. USE, MAKE). Abstract representations of actions ("one-hot" encoding) used in the other two experiments, were replaced with the joints values recorded from the iCub robot sensors. Experimental results showed that motor primitives have different activation patterns according to the action's sequence in which they are embedded. Furthermore, the performed simulations suggested that the acquisition of concepts related to abstract action words requires the reactivation of similar internal representations activated during the acquisition of the basic concepts, directly grounded in perceptual and sensorimotor knowledge, contained in the hierarchical structure of the words used to ground the abstract action words.This study was financed by the EU project RobotDoC (235065) from the Seventh Framework Programme (FP7), Marie Curie Actions Initial Training Network

Development of Cognitive Capabilities in Humanoid Robots

Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

Affordances in Psychology, Neuroscience, and Robotics: A Survey

The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interaction, computer vision, and robotics. In this paper, we offer a multidisciplinary perspective on the notion of affordances. We first discuss the main definitions and formalizations of the affordance theory, then we report the most significant evidence in psychology and neuroscience that support it, and finally we review the most relevant applications of this concept in robotics

Detecting Object Affordances with Convolutional Neural Networks

We present a novel and real-time method to detect object affordances from RGB-D images. Our method trains a deep Convolutional Neural Network (CNN) to learn deep features from the input data in an end-to-end manner. The CNN has an encoder-decoder architecture in order to obtain smooth label predictions. The input data are represented as multiple modalities to let the network learn the features more effectively. Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with the state-of-the-art methods that use hand-designed geometric features. Furthermore, we apply our detection method on a full-size humanoid robot (WALK-MAN) to demonstrate that the robot is able to perform grasps after efficiently detecting the object affordances

Nanotechnology for Humans and Humanoids A vision of the use of nanotechnology in future robotics

Humanoids will soon co-exist with humans, helping us at home and at work, assisting elder people, replacing us in dangerous environments and somewhat adding to our personal communication devices the capability to actuate motion. In order for humanoids to be compatible with our everyday tools and our lifestyle it is however mandatory to reproduce (at least partially) the body-mind nexus that makes humans so superior to machines. This requires a totally new approach to humanoid technologies, combining new responsive and soft materials, bioinspired sensors, high efficiency power sources and cognition/intelligence of low computational cost: in other words, an unprecedented merge of nanotechnology, cognition and mechatronics

Making sense of words: a robotic model for language abstraction

Building robots capable of acting independently in unstructured environments is still a challenging task for roboticists. The capability to comprehend and produce language in a 'human-like' manner represents a powerful tool for the autonomous interaction of robots with human beings, for better understanding situations and exchanging information during the execution of tasks that require cooperation. In this work, we present a robotic model for grounding abstract action words (i.e. USE, MAKE) through the hierarchical organization of terms directly linked to perceptual and motor skills of a humanoid robot. Experimental results have shown that the robot, in response to linguistic commands, is capable of performing the appropriate behaviors on objects. Results obtained in case of inconsistency between the perceptual and linguistic inputs have shown that the robot executes the actions elicited by the seen object