How Morphological Computation Shapes Integrated Information in Embodied Agents

The Integrated Information Theory provides a quantitative approach to consciousness and can be applied to neural networks. An embodied agent controlled by such a network influences and is being influenced by its environment. This involves, on the one hand, morphological computation within goal directed action and, on the other hand, integrated information within the controller, the agent's brain. In this article, we combine different methods in order to examine the information flows among and within the body, the brain and the environment of an agent. This allows us to relate various information flows to each other. We test this framework in a simple experimental setup. There, we calculate the optimal policy for goal-directed behavior based on the “planning as inference” method, in which the information-geometric em-algorithm is used to optimize the likelihood of the goal. Morphological computation and integrated information are then calculated with respect to the optimal policies. Comparing the dynamics of these measures under changing morphological circumstances highlights the antagonistic relationship between these two concepts. The more morphological computation is involved, the less information integration within the brain is required. In order to determine the influence of the brain on the behavior of the agent it is necessary to additionally measure the information flow to and from the brain.

Interactions with Artificial Entities Reloaded: 20 Years of Research from a Social Psychological Perspective

Twenty years ago, we reflected on the potential of psychological research in the area of embodied conversational agents and systematized the variables that need to be considered in empirical studies. We gave an outlook on potential and necessary research by taking into account the independent variables behavior and appearance of the embodied agent, by referring to the dependent variables acceptance, efficiency and effects on behavior and summarizing moderating variables such as task and individual differences. Twenty years later, we now give an account on what has been found and how the field has developed – suggesting avenues for future research.

How to train your robot with deep reinforcement learning: lessons we have learned

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

Immersive virtual environments and embodied agents for e-learning applications

Massive Open Online Courses are a dominant force in remote-learning yet suffer from persisting problems stemming from lack of commitment and low completion rates. In this initial study we investigate how the use of immersive virtual environments for Power-Point based informational learning may benefit learners and mimic traditional lectures successfully. We examine the role of embodied agent tutors which are frequently implemented within virtual learning environments. We find similar performance on a bespoke knowledge test and metrics for motivation, satisfaction, and engagement by learners in both real and virtual environments, regardless of embodied agent tutor presence. Our results raise questions regarding the viability of using virtual environments for remote-learning paradigms, and we emphasise the need for further investigation to inform the design of effective remote-learning applications.

Self-reported health and the social body

Since the strong predictive power of self-reported health (SRH) for prospective health and social outcomes has been established, researchers have been in a quest to build a theoretical understanding of this widely used health measure. Current literature based predominantly in a biomedical perspective asserts a linear relationship between physical conditions and perception of health. Discrepancies from this expected relationship are considered an important weakness of SRH. Systematic discrepancies between physical conditions and reporting of SRH have been documented across different socio-economic groups. Evidence identified for educational groups shows that for the same level of health status, lower-educated groups report poorer levels of perceived health. This raised doubts whether it is useful to use SRH to measure social inequalities in health within and between countries. To date, sociologists of health have not engaged in the discussion of reporting heterogeneity in SRH. After reviewing existing evidence, we contend that the discrepancy in SRH reporting across social groups argued to be a weakness of SRH as a health measure is a strength from a sociological perspective. SRH as a social measure of health is a better predictor than objective measures of health precisely because it captures the lived experience of the embodied agent.

EARL—Embodied Agent-Based Robot Control Systems Modelling Language

The paper presents the Embodied Agent-based Robot control system modelling Language (EARL). EARL follows a Model-Driven Software Development approach (MDSD), which facilitates robot control system development. It is based on a mathematical method of robot controller specification, employing the concept of an Embodied Agent, and a graphical modelling language: System Modelling Language (SysML). It combines the ease of use of SysML with the precision of mathematical specification of certain aspects of the designed system. It makes the whole system specification effective, from the point of view of the time needed to create it, conciseness of the specification and the possibility of its analysis. By using EARL it is possible to specify systems both with fixed and variable structure. This was achieved by introducing a generalised system model and presenting particular structures of the system in terms of modelling block configurations adapted by using instances. FABRIC framework was created to support the implementation of EARL-based controllers. EARL is compatible with component based robotic middlewares (e.g., ROS and Orocos).

The “embreathment” illusion highlights the role of breathing in corporeal awareness

Recent theories posit that physiological signals contribute to corporeal awareness, the basic feeling that one has a body (body ownership) that acts according to one’s will (body agency) and occupies a specific position (body location). Combining physiological recordings with immersive virtual reality, we found that an ecological mapping of real respiratory patterns onto a virtual body illusorily changes corporeal awareness. This new way of inducing a respiratory bodily illusion, called “embreathment,” revealed that breathing is almost as important as visual appearance for inducing body ownership and more important than any other cue for body agency. These effects were moderated by individual levels of interoception, as assessed through a standard heartbeat-counting task and a new “pneumoception” task. By showing that respiratory, visual, and spatial signals exert a specific and weighted influence on the fundamental feeling that one is an embodied agent, we pave the way for a comprehensive hierarchical model of corporeal awareness. NEW & NOTEWORTHY Our body is the only object we sense from the inside; however, it is unclear how much inner physiology contributes to the global sensation of having a body and controlling it. We combine respiration recordings with immersive virtual reality and find that making a virtual body breathe like the real body gives an illusory sense of ownership and agency over the avatar, elucidating the role of a key physiological process like breathing in corporeal awareness.