Dopamine, affordance and active inference.

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level

06231 Abstracts Collection -- Towards Affordance-Based Robot Control

From June 5 to June 9, 2006, the Dagstuhl Seminar 06231 ``Towards Affordance-Based Robot Control\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. %The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available. Additionally, papers related to a selection of the above-mentioned presentations willbe published in a proceedings volume (Springer LNAI) early in 2007

Matalaulotteisen affordanssiesityksen oppiminen ja tämän hyödyntäminen robottijärjestelmän koulutuksessa

The development of data-driven approaches, such as deep learning, has led to the emergence of systems that have achieved human-like performance in wide variety of tasks. For robotic tasks, deep data-driven models are introduced to create adaptive systems without the need of explicitly programming them. These adaptive systems are needed in situations, where task and environment changes remain unforeseen. Convolutional neural networks (CNNs) have become the standard way to process visual data in robotics. End-to-end neural network models that operate the entire control task can perform various complex tasks with little feature engineering. However, the adaptivity of these systems goes hand in hand with the level of variation in the training data. Training end-to-end deep robotic systems requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issue by employing a deep neural network with a modular architecture, consisting of separate perception, policy, and trajectory parts. Each part of the system is trained fully on synthetic data or in simulation. The data is exchanged between parts of the system as low-dimensional representations of affordances and trajectories. The performance is then evaluated in a zero-shot transfer scenario using the Franka Panda robotic arm. Results demonstrate that a low-dimensional representation of scene affordances extracted from an RGB image is sufficient to successfully train manipulator policies.Tietopohjaisten oppimismenetelmien etenkin syväoppimisen viimeaikainen kehitys on synnyttänyt järjestelmiä, jotka ovat saavuttaneet ihmistasoisen suorituskyvyn ihmisälyä vaativissa tehtävissä. Syväoppimiseen pohjautuvia robottijärjestelmiä ollaan kehitetty, jotta ympäristön ja tehtävän muutoksiin mukautuvaisempia robotteja voitaisiin ottaa käyttöön. Konvoluutioneuroverkkojen käyttö kuvatiedon käsittelyssä robotiikassa on yleistä. Neuroverkkomallit, jotka käsittelevät anturitietoa ja suorittavat päätöksenteon ja säädön, voivat oppia monimutkaisia tehtäviä ilman käsin tehtyä kehitystyötä. Näiden järjestelmien kyky mukautua ympäristön muutoksiin on kuitenkin suoraan verrannollinen koulutustiedon monimuotoisuuteen. Syväoppimiseen pohjautuva robottijärjestelmä vaatii oppiakseen suuren määrän ympäristö-, tehtävä-, ja laitteisto-ominaista koulutustietoa, mikä joudutaan yleensä kerätä tehottomasti käsin. Tämän työn tarkoitus on esittää ratkaisu yllämainittuun tehottomuuteen. Esittelemme neuroverkkoarkkitehtuurin, joka koostuu kolmesta erillisestä komponentista. Nämä komponentit koulutetaan erikseen ja koulutus ollaan ainoastaan toteutettu simulaatiossa tai synteettisellä tiedolla ilman fyysisen maailman lisäkouluttautumista Ensimmäinen komponentti tuottaa RGB-kuvasta matalaulotteisen affordanssiesityksen. Tämän esityksen pohjalta toinen komponentti tuottaa matalaulotteisten liikerataesityksen. Kolmas komponentti luo tämän esityksen pohjalta täysimittaisen liikeradan teollisuusrobotille. Järjestelmän suorituskykyä arvioidaan fyysisessä ympäristössä ilman lisäkoulutusta Franka Panda -teollisuusrobotilla. Tulokset osoittavat, että kuvatieto voidaan esittää matalaulotteisena affordanssiesityksenä ja tätä esitystä voidaan käyttää säätötehtävän oppimiseen

Using learned affordances for robotic behavior development

“Developmental robotics” proposes that, instead of trying to build a robot that shows intelligence once and for all, what one must do is to build robots that can develop. These robots should be equipped with behaviors that are simple but enough to bootstrap the system. Then, as the robot interacts with its environment, it should display increasingly complex behaviors. In this paper, we propose such a development scheme for a mobile robot. J.J. Gibson’s concept of “affordances” provides the basis of this development scheme, and we use a formalization of affordances to make the robot learn about the dynamics of its interactions with its environment. We show that an autonomous robot can start with pre-coded primitive behaviors, and as it executes its behaviors randomly in an environment, it can learn the affordance relations between the environment and its behaviors. We then present two ways of using these learned structures, in achieving more complex, intentional behaviors. In the first case, the robot still uses its pre-coded primitive behaviors only, but the sequencing of these primitive behaviors are such that new more complex behaviors emerge. In the second case, the robot makes a “blending” of its pre-coded primitive behaviors to create new behaviors that can be more effective in reaching its goal than any of the pre-coded behaviors

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl