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

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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

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users

Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)

Comparison of motor-based versus visual sensory representations in object recognition tasks

Various works have demonstrated the usage of action as a critical component in allowing autonomous agents to learn about objects in the environment. The importance of memory becomes evident when these agents try to learn about complex objects. This necessity primarily stems from the fact that simpler agents behave reactively to stimuli in their attempt to learn about the nature of the object. However, complex objects have the property of giving rise to temporally varying sensory data as the agent interacts with the object. Therefore, reactive behavior becomes a hindrance in learning these complex objects, thus, prompting the need for memory. A straightforward approach to memory, visual memory, is where sensory data is directly represented. Another mechanism is skill-based memory or habit formation. In the latter mechanism the sequence of actions performed for a task is retained. The main hypothesis of this thesis is that since action seems to play an important role in simple perceptual understanding it may also serve as a good memory representation. In order to test this hypothesis a series of comparative tests were carried out to determine the merits of each of these representations. It turns out that skill memory performs significantly better at recognition tasks than visual memory. Furthermore, it was demonstrated in a related experiment that action forms a good intermediate representation of the sensory data. This provides support to theories that propose that various sensory modalities can ideally be represented in terms of action. This thesis successfully extends action to the role of understanding of complex objects

Slowness learning for curiosity-driven agents

In the absence of external guidance, how can a robot learn to map the many raw pixels of high-dimensional visual inputs to useful action sequences? I study methods that achieve this by making robots self-motivated (curious) to continually build compact representations of sensory inputs that encode different aspects of the changing environment. Previous curiosity-based agents acquired skills by associating intrinsic rewards with world model improvements, and used reinforcement learning (RL) to learn how to get these intrinsic rewards. But unlike in previous implementations, I consider streams of high-dimensional visual inputs, where the world model is a set of compact low-dimensional representations of the high-dimensional inputs. To learn these representations, I use the slowness learning principle, which states that the underlying causes of the changing sensory inputs vary on a much slower time scale than the observed sensory inputs. The representations learned through the slowness learning principle are called slow features (SFs). Slow features have been shown to be useful for RL, since they capture the underlying transition process by extracting spatio-temporal regularities in the raw sensory inputs. However, existing techniques that learn slow features are not readily applicable to curiosity-driven online learning agents, as they estimate computationally expensive covariance matrices from the data via batch processing. The first contribution called the incremental SFA (IncSFA), is a low-complexity, online algorithm that extracts slow features without storing any input data or estimating costly covariance matrices, thereby making it suitable to be used for several online learning applications. However, IncSFA gradually forgets previously learned representations whenever the statistics of the input change. In open-ended online learning, it becomes essential to store learned representations to avoid re- learning previously learned inputs. The second contribution is an online active modular IncSFA algorithm called the curiosity-driven modular incremental slow feature analysis (Curious Dr. MISFA). Curious Dr. MISFA addresses the forgetting problem faced by IncSFA and learns expert slow feature abstractions in order from least to most costly, with theoretical guarantees. The third contribution uses the Curious Dr. MISFA algorithm in a continual curiosity-driven skill acquisition framework that enables robots to acquire, store, and re-use both abstractions and skills in an online and continual manner. I provide (a) a formal analysis of the working of the proposed algorithms; (b) compare them to the existing methods; and (c) use the iCub humanoid robot to demonstrate their application in real-world environments. These contributions together demonstrate that the online implementations of slowness learning make it suitable for an open-ended curiosity-driven RL agent to acquire a repertoire of skills that map the many raw pixels of high-dimensional images to multiple sets of action sequences

Perception and Hierarchical Dynamics

In this paper, we suggest that perception could be modeled by assuming that sensory input is generated by a hierarchy of attractors in a dynamic system. We describe a mathematical model which exploits the temporal structure of rapid sensory dynamics to track the slower trajectories of their underlying causes. This model establishes a proof of concept that slowly changing neuronal states can encode the trajectories of faster sensory signals. We link this hierarchical account to recent developments in the perception of human action; in particular artificial speech recognition. We argue that these hierarchical models of dynamical systems are a plausible starting point to develop robust recognition schemes, because they capture critical temporal dependencies induced by deep hierarchical structure. We conclude by suggesting that a fruitful computational neuroscience approach may emerge from modeling perception as non-autonomous recognition dynamics enslaved by autonomous hierarchical dynamics in the sensorium