Discovering Motion Flow by Temporal-Informational Correlations in Sensors

A method is presented for adapting the sensors of a robot to its current environment and to learn motion flow detection by observing the informational relations between sensors and actuators. Examples are shown where the robot learns to detect motion flow from sensor data generated by its own movement

Learning motor dependent Crutchfield's information distance to anticipate changes in the topology of sensory body maps

International audienceWhat can a robot learn about the structure of its own body when he does not already know the semantics, the type and the position of its sensors and motors? Previous work has shown that an information theoretic approach, based on pairwise Crutchfield's information distance on sensorimotor channels, could allow to measure the informational topology of the set of sensors, i.e. reconstruct approximately the topology of the sensory body map. In this paper, we argue that the informational sensors topology changes with motor configurations in many robotic bodies, but yet, because measuring Crutchfield's distance is very time consuming, it is impossible to remeasure the body's topology for each novel motor configuration. Rather, a model should be learnt that allows the robot to predict Crutchfield's informational distances, and thus anticipate informational body maps, for novel motor configurations. We present experiments showing that learning motor dependent Crutchfield distances can indeed be achieved

Symmetry: a basis for sensorimotor reconstruction

technical reportGiven a set of unknown sensors and actuators, sensorimotor reconstruction is achieved by exploiting relations between the sensor data and the actuator control data to determine sets of similar sensors, sets of similar actuators, necessary relations between them, as well as sensorimotor relations to the environment. Several Author's have addressed this problem, and we propose here a principled approach that exploits various symmetries and that achieves more efficient and robust results. A theoretical position is defined, the approach shown more efficient than previous work, and experimental results given

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

Self-organizing particle systems

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Advances in Complex Systems following peer review. The version of record, Malte Harder and Daniel Polani, ‘Self-organizing particle systems’, Advs. Complex Syst. 16, 1250089, published October 22, 2012, is available online via doi: https://doi.org/10.1142/S0219525912500890 Published by World Scientific Publishing.The self-organization of cells into a living organism is a very intricate process. Under the surface of orchestrating regulatory networks there are physical processes which make the information processing possible, that is required to organize such a multitude of individual entities. We use a quantitative information theoretic approach to assess self-organization of a collective system. In particular, we consider an interacting particle system, that roughly mimics biological cells by exhibiting differential adhesion behavior. Employing techniques related to shape analysis, we show that these systems in most cases exhibit self-organization. Moreover, we consider spatial constraints of interactions, and additionaly show that particle systems can self-organize without the emergence of pattern-like structures. However, we will see that regular pattern-like structures help to overcome limitations of self-organization that are imposed by the spatial structure of interactions.Peer reviewe

Calibration from statistical properties of the visual world

What does a blind entity need in order to determine the geometry of the set of photocells that it carries through a changing lightfield? In this paper, we show that very crude knowledge of some statistical properties of the environment is sufficient for this task. We show that some dissimilarity measures between pairs of signals produced by photocells are strongly related to the angular separation between the photocells. Based on real-world data, we model this relation quantitatively, using dissimilarity measures based on the correlation and conditional entropy. We show that this model allows to estimate the angular separation from the dissimilarity. Although the resulting estimators are not very accurate, they maintain their performance throughout different visual environments, suggesting that the model encodes a very general property of our visual world. Finally, leveraging this method to estimate angles from signal pairs, we show how distance geometry techniques allow to recover the complete sensor geometry

Soft Morphological Computation

Soft Robotics is a relatively new area of research, where progress in material science has powered the next generation of robots, exhibiting biological-like properties such as soft/elastic tissues, compliance, resilience and more besides. One of the issues when employing soft robotics technologies is the soft nature of the interactions arising between the robot and its environment. These interactions are complex, and the their dynamics are non-linear and hard to capture with known models. In this thesis we argue that complex soft interactions can actually be beneficial to the robot, and give rise to rich stimuli which can be used for the resolution of robot tasks. We further argue that the usefulness of these interactions depends on statistical regularities, or structure, that appear in the stimuli. To this end, robots should appropriately employ their morphology and their actions, to influence the system-environment interactions such that structure can arise in the stimuli. In this thesis we show that learning processes can be used to perform such a task. Following this rationale, this thesis proposes and supports the theory of Soft Morphological Computation (SoMComp), by which a soft robot should appropriately condition, or ‘affect’, the soft interactions to improve the quality of the physical stimuli arising from it. SoMComp is composed of four main principles, i.e.: Soft Proprioception, Soft Sensing, Soft Morphology and Soft Actuation. Each of these principles is explored in the context of haptic object recognition or object handling in soft robots. Finally, this thesis provides an overview of this research and its future directions.AHDB CP17

Quantum Uncertainty Reduction (QUR) Theory of Attended Access and Phenomenal Consciousness

In this dissertation I defend a theory of perceptual consciousness titled “Quantum Uncertainty Reduction” (QUR[1]) Theory of Attended Access and Phenomenal Consciousness.” Consciousness is widely perceived as a phenomenon that poses a special explanatory problem for science. The problem arises in the apparent rift between an immediate first-person acquaintance with consciousness and our lack of ability to provide an objective/scientific third-person characterization of consciousness. I begin by reviewing philosophical ideas of Ned Block, David Chalmers and Jesse Prinz whose characterizations of consciousness provide a conceptual framework that the proposed theory aims to satisfy. Block and Chalmers argue that consciousness is a mongrel concept combining two distinct aspects: access and phenomenal consciousness, while Jesse Prinz’s argues for the central role of attention in engendering consciousness. Since the proposed solution is an aspect of quantum information processing in a mechanism, I discuss and adopt methodological approach of the use of mechanisms in scientific explanations developed by William Bechtel, Carl Craver and others. I outline a mechanism based on Shannon Communication System and enhanced with Bayesian predictive processing developed by Carl Friston, Jacob Hohwy, and Andy Clark as well as Control Theory by Rick Grush. Based on views of Marcin Miłkowski, Gualtiero Piccinini and others on information processing in physical systems, I argue that the suggested mechanism implements physical information processing or computation. After a brief overview of relevant aspects of quantum theory, I review recent developments that aim to reconstruct quantum theory by using epistemic approach to explain the nature of quantum states vs. the traditional ontic one. I adopt the epistemic approach and argue that by performing a functional analysis of physical computation in the suggested mechanism we can identify a certain process as involving processing/manipulation of quantum and classical information. I further suggest that the central aspect of the process, namely, quantum uncertainty reduction gives rise to qualitative properties of phenomenal and access consciousness. Further, I compare the suggested information processing formulation of Access and Phenomenal consciousness with those of Block and Chalmers, that are, correspondingly, non-functional and non-physical. I argue that my conceptualization is preferable since it gives a functional and physical account of phenomenal and access consciousness while accommodating thought experiments that Block and Chalmers use to argue for their views on consciousness. Finally, while largely agreeing with the where and when of consciousness of Prinz’s AIR (Attended Intermediate Representations) theory of consciousness, QUR theory offers new arguments for an extended where and more nuanced when of phenomenal consciousness. [1] Pronounced as “cure.