Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe

Sensorimotor embedding : a developmental approach to learning geometry

textA human infant facing the blooming, buzzing confusion of the senses grows up to be an adult with common-sense knowledge of geometry; this knowledge then allows her to describe the shapes of objects, the layouts of places, and the relative locations of things naturally and effortlessly. In robotics, such knowledge is usually built in by a human designer who needs to solve complex engineering problems of sensor calibration and inference. In contrast, this dissertation presents a model for how autonomous agents can form an understanding of geometry the same way infants do: by learning from early unstructured sensorimotor experience. Through a framework called sensorimotor embedding, an agent reconstructs knowledge of its own sensor structure, the local geometry of the world, and the pose of objects within the world. The validity of this knowledge is demonstrated directly through Procrustes analysis and indirectly by using it to solve the mountain car task with different morphologies. The dissertation demonstrates how sensorimotor embedding can serve as a robust approach for acquiring geometric knowledge.Computer Science

From locomotion to cognition: Bridging the gap between reactive and cognitive behavior in a quadruped robot

The cognitivistic paradigm, which states that cognition is a result of computation with symbols that represent the world, has been challenged by many. The opponents have primarily criticized the detachment from direct interaction with the world and pointed to some fundamental problems (for instance the symbol grounding problem). Instead, they emphasized the constitutive role of embodied interaction with the environment. This has motivated the advancement of synthetic methodologies: the phenomenon of interest (cognition) can be studied by building and investigating whole brain-body-environment systems. Our work is centered around a compliant quadruped robot equipped with a multimodal sensory set. In a series of case studies, we investigate the structure of the sensorimotor space that the application of different actions in different environments by the robot brings about. Then, we study how the agent can autonomously abstract the regularities that are induced by the different conditions and use them to improve its behavior. The agent is engaged in path integration, terrain discrimination and gait adaptation, and moving target following tasks. The nature of the tasks forces the robot to leave the ``here-and-now'' time scale of simple reactive stimulus-response behaviors and to learn from its experience, thus creating a ``minimally cognitive'' setting. Solutions to these problems are developed by the agent in a bottom-up fashion. The complete scenarios are then used to illuminate the concepts that are believed to lie at the basis of cognition: sensorimotor contingencies, body schema, and forward internal models. Finally, we discuss how the presented solutions are relevant for applications in robotics, in particular in the area of autonomous model acquisition and adaptation, and, in mobile robots, in dead reckoning and traversability detection

Saving reason : Jurgen Habermas's synthesis of western philosophy.

This dissertation argues that Jürgen Habermas’s philosophy of communicative reason successfully defends the Enlightenment notion of Reason as the vehicle of truth and progress, while integrating Postmodern insights into the illusory nature of metaphysical foundations. Habermas discards the Enlightenment philosophy of the subject with its subjective reasoning, to create his paradigm of mutual understanding using intersubjective reasoning. In so doing, Habermas integrates philosophical hermeneutics and the Linguistic Turn, while using Pragmatism to avoid the Postmodern danger of relativism. The Enlightenment philosophy of the subject as developed from Descartes through Hegel entails aporias of subjectivism. The hermeneutic turn in philosophy reduced subjectivism by de-reifying the division of the objective and subjective worlds, and by including interpersonal learning within its paradigm. The Linguistic Turn in philosophy highlighted the linguistic nature of all knowledge and truth, threatening to relativize both, with their validity limited to a particular language and culture. The legacy of Nietzsche transmitted through Foucault highlights the irrational motivations behind all reasoning, which is reduced to being the tool of selfish power. Gadamer adds his voice both to the linguistification of knowledge and to the aesthetization of rational judgment. Peirce, however, while accepting the linguistification of truth, emphasizes the practical evidence of truth statements as a criterion of their validity. Into this philosophical mixture, Habermas, integrates speech-act theory and theories of cognitive-moral development to create his theory of communicative reason, which grounds the validity of statements on illocutionary speech, but retains non-linguistic experience as a foundation for truth. While giving a nod to non-rational influences on reasoning, Habermas give little attention to this in his philosophy, and I outline the elements of psychoanalytic theory that should be integrated into his philosophy to make it less rationalistic

Subjective Mapping

There are a variety of domains where it is desirable to learn a representation of an environment defined by a stream of sensori-motor experience. This dissertation introduces and formalizes subjective mapping, a novel approach to this problem. A learned representation is subjective if it is constructed almost entirely from the experience stream, minimizing the requirement of additional domain-specific information (which is often not readily obtainable). In many cases the observational data may be too plentiful to be feasibly stored. In these cases, a primary feature of a learned representation is that it be compact---summarizing information in a way that alleviates storage demands. Consequently, the first key insight of the subjective mapping approach is to phrase the problem as a variation of the well-studied problem of dimensionality reduction. The second insight is that knowing the effects of actions is critical to the usefulness of a representation. Therefore enforcing that actions have a consistent and succinct form in the learned representation is also a key requirement. This dissertation presents a new framework, action respecting embedding (ARE), which builds on a recent effective dimensionality reduction algorithm called maximum variance unfolding, in order to solve the newly introduced subjective mapping problem. The resulting learned representations are shown to be useful for reasoning, planning and localization tasks. At the heart of the new algorithm lies a semidefinite program leading to questions about ARE's ability to handle sufficiently large input sizes. The final contribution of this dissertation is to provide a divide-and-conquer algorithm as a first step to addressing this issue

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Casual Reasoning : A Social Ecological Look at Human Cognition and Common Sense

This thesis promotes a pragmatist and ecological approach to human cognition and concepts. Namely, that our conceptual system primarily tracks affordances and other causal properties that have pragmatic relevance to us as embodied and active agents. The bulk of the work aims to show how various research programs in cognitive psychology naturally intersect and complement each other under this theoretical standpoint, which is influenced by enactivist and embodied approaches to cognitive science as well as linguistic pragmatism. The term ”ecological” in the title refers to an approach that emphasizes the interaction of agents and their environment and ”social ecology” means that this includes social interaction between agents and that our material environment is extensively a cultural product, constantly reproduced and altered through cultural behavior. The extent of the argument is not supposed to be confined to the theoretical psychology and philosophy of science but has somewhat wider motives pertaining to philosophy of language and knowledge. I do not attempt to reform extant theories of cognitive processing and representation (unless arguments against logical computationalism are still considered reformist these days) but to explain the nature of conceptual understanding. I take it that having a concept is principally not having a particular information structure in one’s brain but rather a set of interlocking capacities that support intentional action. In effect, I claim that conceptual understanding should be understood as a cognitive skill and psychological research on concepts should not identify concepts as static information structures but as capacities which are integral parts of procedural knowledge that support skillful know-how in situated action. Conceptual mental representations deal with information but such information structures are active constructs that cannot be understood without pragmatic and ecological perspective on human cognition. I argue for the claim, earlier proposed for instance by Eleanor Rosch, that contexts or situations are the proper unit that categorization research needs to concentrate on. In accordance with Edouard Machery’s well-known claim, I conceive classical category theories of cognitive science, namely prototype, exemplar, and knowledge accounts, to tap real cognitive phenomena; however, pace Machery I aim to show that they do not form distinct conceptual representations but rather participate in human conceptual capacities as interlocking component processes. The main problem with theories that emphasize situated direct interaction with the environment is to explain abstract and symbolic reasoning. One theoretically promising way to resolve the issue is to invoke some version of the dual-process theories of cognition; that is, to explain rule-based, theoretical, and symbolic reasoning by resorting to a distinct cognitive system, which is more or less dedicated to those kind of tasks. While dual-process theories seem to license such a move, they can work only as a partial solution because expert scientific reasoning , for example, necessitates implicit skills just like any area of expertise. Second, commonsense reasoning is partly schematic and utilizes theoretical concepts. As an alternative explanation, I offer a hypothesis influenced by philosophical linguistic pragmatism which posits that discursive reasoning is incrementally learned tacit know-how in cultural praxis, which determines how we understand linguistic concepts. This interactive know-how exploits mostly the same cognitive mechanisms as situated and pragmatic procedural knowledge. The explanation has immediate implications for the analytic philosophy of language. When we interpret a text or engage in conceptual analysis, our conscious conceptual interpretation of the associated contents is a product of implicit processes intimately tied with procedural knowledge; in short, explicit know-that is rooted in implicit know-how