Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

The implications of embodiment for behavior and cognition: animal and robotic case studies

In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

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Hybrid hierarchical learning for solving complex sequential tasks using the robotic manipulation network ROMAN

Solving long sequential tasks remains a non-trivial challenge in the field of embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is a notable open problem and continues to be an active area of research. In this work, we present a hybrid hierarchical learning framework, the robotic manipulation network ROMAN, to address the challenge of solving multiple complex tasks over long time horizons in robotic manipulation. By integrating behavioural cloning, imitation learning and reinforcement learning, ROMAN achieves task versatility and robust failure recovery. It consists of a central manipulation network that coordinates an ensemble of various neural networks, each specializing in different recombinable subtasks to generate their correct in-sequence actions, to solve complex long-horizon manipulation tasks. Our experiments show that, by orchestrating and activating these specialized manipulation experts, ROMAN generates correct sequential activations accomplishing long sequences of sophisticated manipulation tasks and achieving adaptive behaviours beyond demonstrations, while exhibiting robustness to various sensory noises. These results highlight the significance and versatility of ROMAN’s dynamic adaptability featuring autonomous failure recovery capabilities, and underline its potential for various autonomous manipulation tasks that require adaptive motor skills

The Problem of Mental Action

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference