Max Lungarella

Self-Organization, Embodiment, and Biologically Inspired Robotics

Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

Developmental robotics: a survey

Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

The challenges ahead for bio-inspired 'soft' robotics

Soft materials may enable the automation of tasks beyond the capacities of current robotic technology.

Body Schema in Robotics: A Review

How is our body imprinted in our brain? This seemingly simple question is a subject of investigations of diverse disciplines, psychology, and philosophy originally complemented by neurosciences more recently. Despite substantial efforts, the mysteries of body representations are far from uncovered. The most widely used notions-body image and body schema-are still waiting to be clearly defined. The mechanisms that underlie body representations are coresponsible for the admiring capabilities that humans or many mammals can display: combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These features are also desirable in robots. This paper surveys the body representations in biology from a functional or computational perspective to set ground for a review of the concept of body schema in robotics. First, we examine application-oriented research: how a robot can improve its capabilities by being able to automatically synthesize, extend, or adapt a model of its body. Second, we summarize the research area in which robots are used as tools to verify hypotheses on the mechanisms underlying biological body representations. We identify trends in these research areas and propose future research directions.

Motor Skill Acquisition Under Environmental Perturbations: On the Necessity of Alternate Freezing and Freeing of Degrees of Freedom:

In a recent study on the pendulation of a small-sized humanoid robot (Lungarella & Berthouze, 2002a, b), we provided experimental evidence that starting with fewer degrees of freedom enables a more efficient exploration of the sensorimotor space during the acquisition of a task. The study came as support for the well-established framework of Bernstein (1967), namely that of an initial freezing of the distal degrees of freedom, followed by their progressive release and the exploitation of environmental and body dynamics. In this paper, we revisit our study by introducing a nonlinear coupling between environment and system. Under otherwise unchanged experimental conditions, we show that a single phase of freezing and subsequent freeing of degrees of freedom is not sufficient to achieve optimal performance, and instead, alternate freezing and freeing of degrees of freedom is required. The interest of this result is twofold: (1) it confirms the recent observation by Newell & Vaillancourt (2001) that Bernstein’s (1967) framework may be too narrow to account for real data; (2) it suggests that perturbations that push the system outside its postural stability or increase the task complexity may be the mechanism that triggers alternate freezing and freeing of degrees of freedom.

ECCE1: The first of a series of anthropomimetic musculoskeletal upper torsos

The human body was not designed by engineers and the way in which it is built poses enormous control problems. Its complexity challenges the ability of classical control theory to explain human movement as well as the development of human motor skills. It is our working hypothesis that the engineering paradigm for building robots places severe limitations on the kinds of interactions such robots can engage in, on the knowledge they can acquire of their environment, and therefore on the nature of their cognitive engagement with the environment. This paper describes the design of an anthropomimetic humanoid upper torso, ECCE1, built in the context of the ECCEROBOT project. The goal of the project is to use this platform to test hypotheses about human motion as well as to compare its performance with that of humans, whether at the mechanical, behavioural or cognitive level.

On the interplay between morphological, neural, and environmental dynamics: A robotic case study

The robust and adaptive behavior exhibited by natural organisms is the result of a complex interaction between various plastic mechanisms acting at different time scales. So far, researchers have concentrated on one or another of these mechanisms, but little has been done toward integrating them into a unified framework and studying the result of their interplay in a real-world environment. In this article, we present experiments with a small humanoid robot that learns to swing. They illustrate that the exploitation of neural plasticity, entrainment to physical dynamics, and body growth (where each mechanism has a specific time scale) leads to a more efficient exploration of the sensorimotor space and eventually to a more adaptive behavior. Such a result is consistent with observations in developmental psychology.

An artificial whisker sensor for robotics

In this paper, we present a first series of experiments with prototype artificial whiskers that have been developed in our laboratory. These experiments have been inspired by neuroscience research on real rats. In spite of the enormous potential of whiskers, they have to date not been systematically investigated and exploited by roboticists. Although the transduction mechanism is simple and straightforward, and the whiskers are currently used in a passive way only, the dynamics of the sensory signals resulting from the interaction with various textured surfaces is complex and has a rich information content. The experiments provide the foundation for future work including active sensing, whisker arrays, and cross-modal integration.

50 Years of Artificial Intelligence: essays dedicated to the 50th anniversary of artificial intelligence (Festschrift)

Historical and Philosphical Issues.- AI in the 21st Century - With Historical Reflections.- The Physical Symbol System Hypothesis: Status and Prospects.- Fifty Years of AI: From Symbols to Embodiment - and Back.- 2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years.- Evolutionary Humanoid Robotics: Past, Present and Future.- Philosophical Foundations of AI.- On the Role of AI in the Ongoing Paradigm Shift within the Cognitive Sciences.- Information Theory and Quantification.- On the Information Theoretic Implications of Embodiment - Principles and Methods.- Development Via Information Self-structuring of Sensorimotor Experience and Interaction.- How Information and Embodiment Shape Intelligent Information Processing.- Preliminary Considerations for a Quantitative Theory of Networked Embodied Intelligence.- A Quantitative Investigation into Distribution of Memory and Learning in Multi Agent Systems with Implicit Communications.- Morphology and Dynamics.- AI in Locomotion: Challenges and Perspectives of Underactuated Robots.- On the Task Distribution Between Control and Mechanical Systems.- Bacteria Integrated Swimming Microrobots.- Adaptive Multi-modal Sensors.- Neurorobotics.- What Can AI Get from Neuroscience?.- Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior.- Adaptive Behavior Control with Self-regulating Neurons.- Brain Area V6A: A Cognitive Model for an Embodied Artificial Intelligence.- The Man-Machine Interaction: The Influence of Artificial Intelligence on Rehabilitation Robotics.- Machine Intelligence, Cognition, and Natural Language Processing.- Tests of Machine Intelligence.- A Hierarchical Concept Oriented Representation for Spatial Cognition in Mobile Robots.- Anticipation and Future-Oriented Capabilities in Natural and Artificial Cognition.- Computer-Supported Human-Human Multilingual Communication.- Human-Like Intelligence: Motivation, Emotions, and Consciousness.- A Paradigm Shift in Artificial Intelligence: Why Social Intelligence Matters in the Design and Development of Robots with Human-Like Intelligence.- Intrinsically Motivated Machines.- Curious and Creative Machines.- Applying Data Fusion in a Rational Decision Making with Emotional Regulation.- How to Build Consciousness into a Robot: The Sensorimotor Approach.- Robot Platforms.- A Human-Like Robot Torso ZAR5 with Fluidic Muscles: Toward a Common Platform for Embodied AI.- The iCub Cognitive Humanoid Robot: An Open-System Research Platform for Enactive Cognition.- Intelligent Mobile Manipulators in Industrial Applications:Experiences and Challenges.- Art and AI.- The Dynamic Darwinian Diorama: A Landlocked Archipelago Enhances Epistemology.

How information and embodiment shape intelligent information processing

Embodied artificial intelligence is based on the notion that cognition and action emerge from interactions between brain, body and environment. This chapter sketches a set of foundational principles that might be useful for understanding the emergence (discovery) of intelligence in biological and artificial embodied systems. Special emphasis is placed on information as a crucial resource for organisms and on information theory as a promising descriptive and predictive framework linking morphology, perception, action and neural control.