Unit 1 - Developmental Robotics introduction
DevRob Definition and History
This units will give a definition of Developmental Robotics (DevRob, henceforth) and look at its origins within the field of Artificial Intelligence (e.g. Turing) and the nature-nurture debate in child psychology. It will then discuss the recent estalishment of the DevRob community working on baby robots and autonomous cognitive development. The unit will specifically focus on:
- Turing’s vision of developmental systems;
- Definition and characteristics;
- Nature vs Nurture debate;
- History and establishment of the DevRob community.
Alan Turing, one of the fathers of computer science and Artificial Intelligence (AI), in 1950 already provided a vision of a developmental approach to the simulation of the child’s mind:
«Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain» Alan Turing (1950:440).
But it wasn’t until 50 years later, in early 2000s, that such a vision start to be come real with the establishment of the DevRob community.
«Developmental Robotics is the interdisciplinary approach to the autonomous design of behavioral and cognitive capabilities in artificial agents (robots) that takes direct inspiration from thedevelopmental principles and mechanisms in natural cognitive systems (children)» (Cangelosi & Schlesinger, 2015)
Developmental Robotics characteristics:
- Highly interdisciplinary effort of empirical developmental sciences, e.g. developmental psychology, neuroscience and comparative psychology, and computational and robotics sciences, e.g. robotics and artificial intelligence.
- Developmental sciences provide the empirical bases and data to identify the general developmental principles, mechanisms, models and phenomena guiding the incremental acquisition of cognitive skills.
- The implementation of these principles and mechanisms into a robot’s control architecture and the testing through experiments, where the robot interacts with its physical and social environment simultaneously, permits the validation of such principles and the actual design of complex behavioral and mental capabilities in robots.
Developmental Robotics history and origins:
- First, pioneering work and publications advocating an explicit link between human development and robotics;
- E.g. Sandini, Metta and Konczak (1997), Brooks et al. (1998), Scassellatti (1998), and Asada, MacDorman, Ishiguro and Kuniyoshi (2001).
- The birth of the developmental robotics field traces its origins to the years 2000-2001, in coincidence with two scientific workshops/conference series:
- ICDL: International Conference on Developmental and Learning (first ICDL Workshop in 2000, in Lansing);
- EpiRob: International Workshop on Epigenetic Robotics (first EpiRob Workshop in 2001, in Lund, Sweden).
Developmental Robotics history and origins:
- The two conference series merged efforts in 2011 with 1st Joint ICDL-EpiRob Conference (Frankfurt) and subsequent yearly meetings (2015 is in Providence, USA)Dedicated journal from 2009: IEEE Transactions on Autonomous Mental Development;
- Dedicated newsletter: AMD Newsletter;
- Dedicated Technical Committee of the IEEE Computational Intelligence Society (CIS): AMDTC;
- Website with link to all ICDL-EpiRob conferences: icdl-epirob.org.
“Developmental Robotics” term used in the following reviews:
- Metta, Sandini, Natale & Panerai, 2001; Lungarella, Metta, Pfeifer & Sandini, 2003; Vernon, von Hofsten & Fadiga, 2010; Oudeyer 2012;
- Most recent and comprehensive review in the 2015 volume by Cangelosi and Schlesinger (MIT Press);
- Other names proposed for the same approach/field;
- Cognitive Developmental Robotics (Asada et al. 2001, 2009) for more general cognitive systems approach;
- Autonomous Mental Development (Weng et al. 2001; ICDL Conference series) to stress autonomous aspects of mental (i.e. cognitve) development;
- Epigenetic Robotics (Balkenius, Zlatev, Kozima, Dautenhahn & Breazeal, 2001; Berthouze & Ziemke, 2003; EpiRob Conference Series) to trace its origins and inspiration from Piaget’s Epigenetic Theory.
Developmental Theories and Nature vs. Nurture (i.e. Nativist vs Empiricist) debate informs DevRob field. Nativist (Nature) Theories:
- Children are born with innate, domain-specific knowledge, which is the result of direct influence of the genes on mental development, with little or no influence from environment.
- Chomsky (1956): children are born with Language Acquisition Device for language;
- Leslie (1994): children are born with a theory of mind;
- Wynn (1998): innate knowledge of math concepts.
Empiricist (Nurture) Theories:
- Importance of the social and cultural environment and experience on cognitive development.
- For example:
- E.g. Vygotsky‘s (1978) sociocultural theory, where the role of adults and peers is essential to guide the child to exploit her “zone of proximal development” (the space of the infant’s potential capabilities);
- Bruner‘s socio-cognitive theory of development (Bruner & Helen, 1987) on the importance of social interaction and interpersonal communication in the various learning stages;
- Tomasello (2003) principle of constructivist and emergent development, whereby the child constructs her own language competence through interaction with others
Empiricist (Nurture) Theories:
- Piaget‘s (1971) Epigenetics theory, based on adaptation (assimilation and accommodation) with stage-like, qualitative progression. Piaget’s theory combines nurture mechanisms with empirical developmental mechanisms;
- Thelen and Smith’s (1994) dynamical systems theory of development. This considers the complex, dynamics interaction of various neural, embodiment and environmental factors in the self-organization of cognitive strategies;
The nature/nurture debate and nativist/empiricist theories have significantly influenced other fields interested in the study of intelligence, specifically in artificial intelligence and robotics.
- DevRob uses interidisciplinary approach, inspired by developmental (psychology) mechanisms, for the autonomous design of skills in robots.
- Origins in Turing’s developmental vision.
- Discipline started in 2000, with the first ICDL and EpiRob workshops.
- Within the Nature vs Nurture Debate, DevRob plavces emphasis on empirical, interaction phenomena for cognitive development.
Unit 2 - Principles
There are six general principles that inform the work on DevRob. They will be introduced in this unit, with particular emphasis on 3 of these principles. Content of the unit:
- The six principles.
- Highlight of three key principles:
- Dynamical systems;
- Nonlinear, stages (Piaget);
- Embodied intelligence.
Principle 1: Development as a Dynamical System Thelen and Smith (1994) propose that the development of a child should be viewed as change within a complex dynamic system, where the growing child can generate novel behaviors through its interaction with the environment, and these behavioral states vary in their stability within the complex system. Development as the emergent product of the intricate and dynamic interaction of many decentralized and local interactions related to the child’s growing body, brain and her environment.
Thelen E & Smith L (1994). A Dynamic Systems Approach to the Development of Cognition and Action | The MIT Press. MIT Press
Development as a Dynamical System:
Multicausality: Multicausality when one behavior is determined by the simultaneous and dynamic consequences of various phenomena at the level of the brain, body and environment. Example of dynamic changes in crawling and walking behaviors as multicausality changes in the child’s adaptation to the environment, in response to body growth changes:
- When the child’s body configuration produces sufficient strength and coordination to support its body through the hands and knee posture, but not strong enough for upright walking, the child settles for a crawling strategy to locomote in the environment;
- But when the infant’s body growth results in stronger and more stable legs, the standing and walking behavior emerges as the stable developmental state, which as a consequence destabilizes, and gradually stops, the pattern of crawling.
Development as a Dynamical System.
Nested Timescales: Nested timescales, i.e. neural and embodiment phenomena acting at different timescales, and all affecting development in an intricate, dynamical way. For example the dynamics of the very fast time scale of neural activity (milliseconds) is nested within the dynamics of the other slower timescales such as reaction time during action (seconds or hundreds of milliseconds), learning (after hours or days), and physical body growth (months).
Development as a Dynamical System: A-not-B Error Experiment. This child psychology experiment demonstrates the combined effects of the concepts of multicausality and nested timescales:
- Inspired by Piaget’s object permanence experiment, when one toy is repeatedly hidden under a lid at a location A (right) during the first part of the experiment. Towards the end of the task, the experimenter hides the same toy in the location B (left) for a single trial, and then asks the child to reach for the object.
- Whilst infants older than 12 months have no problem in reaching for the toy in its correct location B, unexpectedly most 8- to 10-month-old infants produce the curious error of looking for the object in the location A. This error is only produced when there is a short delay between hiding and reaching.
Development as a Dynamical System: A-not-B Error Experiment:
- Whilst psychologists such as Piaget have used explanations based on age (stage) differences linked to qualitative changes in the capability to represent objects and space, a computational simulation of the dynamical system model (Thelen, Schöner, Scheier & Smith, 2001) has demonstrated that there are many decentralized factors (multicausality) and timing manipulations (nested timing) affecting such a situation.
- These for example depend on the time delay between hiding and reaching, the properties of the lids on the table, the saliency of the hiding event, the past activity of the infant and her body posture. The systematic manipulation of these factors results in the appearance, stopping and modulation of the A-not-B errors.
Principle 3: Embodied, Situated & Enactive Development «Intelligence cannot merely exist in the form of an abstract algorithm but requires a physical instantiation, a body». Pfeifer & Scheier 1999 The body of the child (or of the robot), and its interaction with the environmental context determines the type of representations, internal models and cognitive strategies learned:
- Embodiment: Fundamental role of the body in cognition and intelligence (Embodied/grounded cognition);
- Situatedness: Role of interaction between the body and its environment;
- Enaction: the organism’s autonomous generation of a model of the world through sensorimotor interactions.
Embodied, Situated and Enactive Development Morphological computation (Pfeifer & Bongard, 2007)
- The organism can exploit the body’s morphological properties (e.g. type of joint, length of limbs, passive/active actuators), and the dynamics of the interaction with the physical environment (e.g. gravity) to produce intelligent behavior.
- One of the best known examples of this is the passive dynamic walker, i.e. bipedal robots that can walk on a slope without any actuator, thus not requiring any explicit control, or bipedal robots only requiring minimal actuation to start movement (McGeer, 1990; Collins et al. 2005).
- The exploitation of morphological computation has important implications for energy consumption optimization in robotics, and for the use of increasing use of compliant actuators and soft robotics material (Pfeifer, Lungarella & Lida, 2012).
Principle 5: Non-linear, stage-like development Development doe snot consist in the linera, incremental improvedemt of skills, but rather it often shows non-lineral trends based around qualitative, sudden changes in performance (stages) For example, the key tenet of Piaget‘s theory is that a child goes through different stages of development, where at each stage the infant develops qualitatively-different and increasingly-complex schemas, the building block of intelligence. Stages are influenced by maturational constraints, determined by genetic influence, and called "epigenetics" in Piaget’s Theory (Piaget, 1971).
Non-linear, stage-like development: Piaget’s four stages of the development of mental capabilities, and abstract thought schemas from sensorimotor knowledge.
- Sensorimotor Stage (Stage 1, 0-2 years), with the acquisition of sensorimotor schemas, e.g. motor reflexes;
- Preoperational Stage (Stage 2, 2-7 years), children acquire egocentric symbolic representations of objects and actions, to represent objects (object permanence task);
- Concrete Operational Stage (Stage 3, 7-11 years) to adopt other people’s perspectives on object representation and perform mental transformation operations on concrete objects (e.g. liquid conservation task);
- Formal Operational Stage (Stage 4, 11+ years) with full abstract thinking capabilities and complex problem solving.
Non-linear, stage-like development: U-Shape phenomenon The (inverted) U-shape phenomenon is an example of such non linearity. This is characterized by an early stage of good performance and low errors, followed by an unexpected decrease in performance, which is subsequently recovered to show high performance. Examples of U-Shaped learning will be discussed in Chapter 8 (language).
See Chapter 1 in Cangelosi & Schlesinger (2015) for further discussion on these three principles and for details on the remaining three principles.
For neural network computational modelling of U-shape learning, see U-shaped curves in development: A PDP approach.
- Six principles permeate the whole DevRob discipline;
- Key principles include the Dynamical Systems approach, the Embodied and Situated principle, and the stage-like development;
- These principle swill be used in the final Lesson (11) to re-asses the state-of-the-art in the field.
Cangelosi A. & Schlesinger M. (2015) Developmental Robotics: From Babies to Robots, MIT Press – Chapter 1
Asada, M., Hosoda, K., Kuniyoshi, Y., et al. (2009). Cognitive developmental robotics: A survey. IEEE Transactions on Autonomous Mental Development, 1, 12-34.
Lungarella, M., Metta, G., Pfeifer, R. and Sandini, G. (2003). Developmental robotics: a survey. Connection Science, 15:151–190.
Thelen, E., Smith L. B. (1994). A Dynamic Systems Approach to the Development of Cognition and Action. Cambridge, MA: MIT Press, 1994.
Pfeifer, R., and J. Bongard. (2007). How the Body Shapes the Way We Think: A New View of Intelligence. Cambridge, MA: MIT press, Bradford Books
Nolfi, S., and D. Floreano (2000). Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT press, Bradford Books
Risorse della lezione
- Introduction to Developmental Robotics
- Robot Definition and Technologies
- Baby Robots
- Intrinsic Motivation
- Visual Development
- Motor-Skill Development
- Social Learning
- Language Learning
- Abstract Knowledge
- Cognitive Architectures
- Future Developments
Immagini slide 14
- Table summarizing the 6 principles of DevRob, and their associated characteristics
- Thelen E & Smith L (1994). A dynamic systems approach to the development of cognition and action. MIT Press