Nonmodular architectures of cognitive systems based on active inference

In psychology and neuroscience it is common to describe cognitive systems as input/output devices where perceptual and motor functions are implemented in a purely feedforward, open-loop fashion. On this view, perception and action are often seen as encapsulated modules with limited interaction between them. While embodied and enactive approaches to cognitive science have challenged the idealisation of the brain as an input/output device, we argue that even the more recent attempts to model systems using closed-loop architectures still heavily rely on a strong separation between motor and perceptual functions. Previously, we have suggested that the mainstream notion of modularity strongly resonates with the separation principle of control theory. In this work we present a minimal model of a sensorimotor loop implementing an architecture based on the separation principle. We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent. These forces can be seen as variables that an agent cannot directly control, i.e., a perturbation from the environment or an interference caused by other agents. As an alternative approach inspired by embodied cognitive science, we then propose a nonmodular architecture based on active inference. We demonstrate the robustness of this architecture to unknown external inputs and show that the mechanism with which this is achieved in linear models is equivalent to integral control

Robotic sensorimotor interaction strategies

Abstract. In this thesis we investigate the mathematical modeling of cognition using sensorimotor transition systems. The focus of the thesis is enactivism, where an agent learns to think through actions. As a theoretical basis for our implementation, we discuss a mathematical model of enactivist cognition, sensorimotor interaction and how they can be used as algorithmic aides for studying theoretical problems in robotic systems. In Chapter 3 of this thesis, we introduce a platform which was developed in the University of Oulu as a software project and explain how enactivism and sensorimotor interaction have been taken advantage of, in developing a 2D platform. This platform enables one to concretely implement and explore different interaction strategies that allow an agent to construct internal models of its surroundings. The agent in the platform is a multi-jointed robotic arm, which maneuvers through an obstacle-filled environment. The robotic arm tries to explore its environment with minimal sensory feedback, using algorithms created by the user of the platform. Our main goal on this thesis is to implement new features to this platform. We implement a memory functionality which allows the robotic arm to store all its performed actions. The memory helps the agent infer to a greater extent its surroundings from a limited sequence of action-observation pairs, and helps it in getting a better grasp of the environment. In addition, we implement other methods and functionalities, such as an obstacle sensor, a graph visualization of the internal models, etc. to enhance the perceptual ability of the robotic arm. In Section 5, we develop an algorithm for a simple 2D environment with no obstacles. Here the robotic arm makes a 360-degree move in four steps to perceive its surroundings and generates a state machine graph to visualize its internal model of the environment. The goal of the algorithm is to build an accurate representation of the environment with the help of memory. Through this algorithm we are able to evaluate the performance of the newly implemented features. We also test the platform through unit testing for finding and resolving bugs.Sensorimotoriset vuorovaikutusstrategiat robotiikassa. Tiivistelmä. Tässä tutkielmassa tutkimme kognition matemaattista mallintamista käyttämällä sensorimotorisia transitio-järjestelmiä. Tutkielman keskiössä on enaktivismi, jossa agentti oppii ajattelemaan toiminnan kautta. Teoreettisena perustana toteutuksellemme käsittelemme matemaattista mallia enaktivistisesta kognitiosta, sensorimotorista vuorovaikutusta ja kuinka niitä voidaan käyttää algoritmien apuvälineinä teoreettisten ongelmien tutkimisessa robottiikkajärjestelmissä. Tutkielman luvussa 3 esittelemme alustan, joka on kehitetty Oulun yliopistossa ohjelmistoprojektina, ja selittämme miten enaktivismia ja sensomotorista vuorovaikutusta on hyödynnetty 2D-alustan kehittämisessä. Alusta mahdollistaa erilaisten vuorovaikutusstrategioiden konkreettisen toteuttamisen ja tutkimisen. Näiden avulla agentti rakentaa sisäisiä malleja ympäristöstään. Alustassa mallinnettu agentti on moninivelinen robottikäsi, joka liikkuu esteitä sisältävässä ympäristössä. Robottikäsi pyrkii tutkimaan ympäristöään minimaalisen sensoritiedon avulla käyttämällä alustan käyttäjän luomia algoritmeja. Tutkielmamme päätavoite on kehittää uusia ominaisuuksia tälle alustalle. Toteutamme muistitoiminnallisuuden, jonka avulla robottikäsi tallentaa kaikki suoritetut toiminnot. Muisti auttaa agenttia päättelemään enemmän ympäristöstään rajoitettujen toiminta-havainto-parien avulla, ja auttaa sitä ympäristön hahmottamisessa. Lisäksi kehitämme muita menetelmiä ja toiminnallisuuksia kuten estesensorin ja sisäisten mallien graafisen visualisoinnin parantaaksemme robottikäden havainnointikykyä. Tutkielman myöhemmässä osassa kehitämme algoritmin yksinkertaiselle 2D-ympäristölle ilman esteitä. Siinä robottikäsi tekee 360 asteen liikkeen neljässä vaiheessa havainnoidakseen ympäristönsä, ja luo tilasiirtymäkaavion visualisoidakseen sisäisen mallinsa ympäristöstä. Algoritmin tavoitteena on rakentaa tarkka malli ympäristöstä muistin avulla. Tämän algoritmin avulla pystymme arvioimaan kehittämiemme uusien ominaisuuksien toimintaa. Testaamme alustaa myös yksikkötesteillä löytääksemme ja korjataksemme virheitä

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

A framework to identify structured behavioral patterns within rodent spatial trajectories

Animal behavior is highly structured. Yet, structured behavioral patterns—or “statistical ethograms”—are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks or motor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments

Active Inference

The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning

Modeling motor control in continuous-time Active Inference: a survey

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matching internal predictions with sensations. Active Inference offers a modern formulation of these ideas, in terms of inferential mechanisms and prediction-error-based control, which can be linked to neural mechanisms of living organisms. This article provides a technical illustration of Active Inference models in continuous time and a brief survey of Active Inference models that solve four kinds of control problems; namely, the control of goal-directed reaching movements, active sensing, the resolution of multisensory conflict during movement and the integration of decision-making and motor control. Crucially, in Active Inference, all these different facets of motor control emerge from the same optimization process - namely, the minimization of Free Energy - and do not require designing separate cost functions. Therefore, Active Inference provides a unitary perspective on various aspects of motor control that can inform both the study of biological control mechanisms and the design of artificial and robotic systems

Generative Models for Active Vision

The active visual system comprises the visual cortices, cerebral attention networks, and oculomotor system. While fascinating in its own right, it is also an important model for sensorimotor networks in general. A prominent approach to studying this system is active inference—which assumes the brain makes use of an internal (generative) model to predict proprioceptive and visual input. This approach treats action as ensuring sensations conform to predictions (i.e., by moving the eyes) and posits that visual percepts are the consequence of updating predictions to conform to sensations. Under active inference, the challenge is to identify the form of the generative model that makes these predictions—and thus directs behavior. In this paper, we provide an overview of the generative models that the brain must employ to engage in active vision. This means specifying the processes that explain retinal cell activity and proprioceptive information from oculomotor muscle fibers. In addition to the mechanics of the eyes and retina, these processes include our choices about where to move our eyes. These decisions rest upon beliefs about salient locations, or the potential for information gain and belief-updating. A key theme of this paper is the relationship between “looking” and “seeing” under the brain's implicit generative model of the visual world

Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles

For autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical self-driving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence. This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach. Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure

Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles

Mención Internacional en el título de doctorFor autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical selfdriving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence. This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach. Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Marco Carli.- Secretario: Víctor González Castro.- Vocal: Nicola Conc