Towards a Platform-Independent Cooperative Human Robot Interaction System: III. An Architecture for Learning and Executing Actions and Shared Plans

Robots should be capable of interacting in a cooperative and adaptive manner with their human counterparts in open-ended tasks that can change in real-time. An important aspect of the robot behavior will be the ability to acquire new knowledge of the cooperative tasks by observing and interacting with humans. The current research addresses this challenge. We present results from a cooperative human-robot interaction system that has been specifically developed for portability between different humanoid platforms, by abstraction layers at the perceptual and motor interfaces. In the perceptual domain, the resulting system is demonstrated to learn to recognize objects and to recognize actions as sequences of perceptual primitives, and to transfer this learning, and recognition, between different robotic platforms. For execution, composite actions and plans are shown to be learnt on one robot and executed successfully on a different one. Most importantly, the system provides the ability to link actions into shared plans, that form the basis of human-robot cooperation, applying principles from human cognitive development to the domain of robot cognitive systems. © 2009-2011 IEEE

Grounding the Meanings in Sensorimotor Behavior using Reinforcement Learning

The recent outburst of interest in cognitive developmental robotics is fueled by the ambition to propose ecologically plausible mechanisms of how, among other things, a learning agent/robot could ground linguistic meanings in its sensorimotor behavior. Along this stream, we propose a model that allows the simulated iCub robot to learn the meanings of actions (point, touch, and push) oriented toward objects in robot’s peripersonal space. In our experiments, the iCub learns to execute motor actions and comment on them. Architecturally, the model is composed of three neural-network-based modules that are trained in different ways. The first module, a two-layer perceptron, is trained by back-propagation to attend to the target position in the visual scene, given the low-level visual information and the feature-based target information. The second module, having the form of an actor-critic architecture, is the most distinguishing part of our model, and is trained by a continuous version of reinforcement learning to execute actions as sequences, based on a linguistic command. The third module, an echo-state network, is trained to provide the linguistic description of the executed actions. The trained model generalizes well in case of novel action-target combinations with randomized initial arm positions. It can also promptly adapt its behavior if the action/target suddenly changes during motor execution

What are the key open challenges for understanding the autonomous cumulative learning of skills?

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Vocal Interactivity in-and-between Humans, Animals, and Robots

Almost all animals exploit vocal signals for a range of ecologically motivated purposes: detecting predators/prey and marking territory, expressing emotions, establishing social relations, and sharing information. Whether it is a bird raising an alarm, a whale calling to potential partners, a dog responding to human commands, a parent reading a story with a child, or a business-person accessing stock prices using Siri, vocalization provides a valuable communication channel through which behavior may be coordinated and controlled, and information may be distributed and acquired. Indeed, the ubiquity of vocal interaction has led to research across an extremely diverse array of fields, from assessing animal welfare, to understanding the precursors of human language, to developing voice-based human–machine interaction. Opportunities for cross-fertilization between these fields abound; for example, using artificial cognitive agents to investigate contemporary theories of language grounding, using machine learning to analyze different habitats or adding vocal expressivity to the next generation of language-enabled autonomous social agents. However, much of the research is conducted within well-defined disciplinary boundaries, and many fundamental issues remain. This paper attempts to redress the balance by presenting a comparative review of vocal interaction within-and-between humans, animals, and artificial agents (such as robots), and it identifies a rich set of open research questions that may benefit from an interdisciplinary analysis

I Reach Faster When I See You Look: Gaze Effects in Human–Human and Human–Robot Face-to-Face Cooperation

Human–human interaction in natural environments relies on a variety of perceptual cues. Humanoid robots are becoming increasingly refined in their sensorimotor capabilities, and thus should now be able to manipulate and exploit these social cues in cooperation with their human partners. Previous studies have demonstrated that people follow human and robot gaze, and that it can help them to cope with spatially ambiguous language. Our goal is to extend these findings into the domain of action, to determine how human and robot gaze can influence the speed and accuracy of human action. We report on results from a human–human cooperation experiment demonstrating that an agent’s vision of her/his partner’s gaze can significantly improve that agent’s performance in a cooperative task. We then implement a heuristic capability to generate such gaze cues by a humanoid robot that engages in the same cooperative interaction. The subsequent human–robot experiments demonstrate that a human agent can indeed exploit the predictive gaze of their robot partner in a cooperative task. This allows us to render the humanoid robot more human-like in its ability to communicate with humans. The long term objectives of the work are thus to identify social cooperation cues, and to validate their pertinence through implementation in a cooperative robot. The current research provides the robot with the capability to produce appropriate speech and gaze cues in the context of human–robot cooperation tasks. Gaze is manipulated in three conditions: Full gaze (coordinated eye and head), eyes hidden with sunglasses, and head fixed. We demonstrate the pertinence of these cues in terms of statistical measures of action times for humans in the context of a cooperative task, as gaze significantly facilitates cooperation as measured by human response times

Mindedness: On the minimal conditions for possessing a mind

This thesis explores the grounds for justifying the ascription of mentality to non-human agents. In the first part, I set my research within the framework of scientific naturalism and the computational theory of mind. Then I argue that while the behaviour of certain agents demands a computational explanation, there is no justification for attributing mentality to them. I use these examples to backup my claim that some authors indulge in unnecessary ascription of mentality to certain animals (e.g. insects) on the main grounds that they possess computational capacities. The second part of my thesis takes up recent literature exploring the line that divides computational agents with and without mentality. More precisely, I criticise the proposals put forward by Fodor, Dretske, Burge, Bermúdez and Carruthers. My main argument takes the form of a reductio ad absurdum by showing that their criteria apply to artefacts to which the attribution of mentality is unjustified. Overall, I conclude that even though the views advanced by the mentioned authors help to elucidate the computational grounds that could make the emergence of a mind possible, they do not offer a satisfactory criterion for the ascription of mentality to some computational agents but not others. In the final part I develop my own proposal for grounding the attribution of mentality. My strategy consists in drawing upon the distinction between personal and subpersonal levels of explanation, according to which properly psychological descriptions have whole-agents as their subject matter, use a distinctive theoretical vocabulary, and are constrained by norms of rationality. After showing that the personal-subpersonal distinction is compatible with a naturalistic framework, I adapt the distinction so that it can be applied to non-human agents, and conclude that it imposes constraints in cognitive architecture that point in the direction of cognitive access, generality and integration