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Sensorimotor Representation Learning for an "Active Self" in Robots: A Model Survey

KUNSTLICHE INTELLIGENZ 2021年第1期35卷 9-35页

作者： Nguyen, Phuong D. H. Georgie, Yasmin Kim Kayhan, Ezgi Eppe, Manfred Hafner, Verena Vanessa Wermter, Stefan Univ Hamburg Dept Informat Vogt Koelln Str 30 D-22527 Hamburg Germany Humboldt Univ Dept Comp Sci Unter Linden 6 D-10019 Berlin Germany Univ Potsdam Dept Dev Psychol Potsdam Germany Max Planck Inst Human Cognit & Brain Sci Leipzig Germany

Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self;and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.

关键词： developmental robotics Body schema Peripersonal space Agency Robot learning

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Modeling Behavior Cycles as a Value System for developmental Robots

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ADAPTIVE BEHAVIOR 2010年第3-4期18卷 237-257页

作者： Merrick, Kathryn E. Univ New S Wales Australian Def Force Acad Sch Engn & Informat Technol Canberra ACT 2600 Australia

The behavior of natural systems is governed by rhythmic behavior cycles at the biological, cognitive, and social levels. These cycles permit natural organisms to adapt their behavior to their environment for survival, behavioral efficiency, or evolutionary advantage. This article proposes a model of behavior cycles as the basis for motivated reinforcement learning in developmental robots. Motivated reinforcement learning is a machine learning technique that incorporates a value system with a trial-anderror learning component. Motivated reinforcement learning is a promising model for developmental robotics because it provides a way for artificial agents to build and adapt their skill-sets autonomously over time. However, new models and metrics are needed to scale existing motivated reinforcement learning algorithms to the complex, real-world environments inhabited by robots. This article presents two such models and an experimental evaluation on four Lego Mindstorms NXT robots. Results show that the robots can evolve measurable, structured behavior cycles adapted to their individual physical forms.

关键词： behavior cycles developmental robotics motivated reinforcement learning neural networks SART networks

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Emergence of eye-hand coordination as a creative process in an artificial developmental agent

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ADAPTIVE BEHAVIOR 2017年第6期25卷 289-314页

作者： Aguilar, Wendy Perez y Perez, Rafael Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Dept Ciencias Comp Ciudad Univ Mexico City 01000 DF Mexico UAM Cuajimalpa Div Ciencias Comunicac & Diseno Dept Tecnol Informac Mexico City DF Mexico

Based on Piaget's theory of cognitive development and an extension of the Engagement-Reflection model of creativity, the developmental Engagement-Reflection (Dev E-R) model characterizes the early cognitive development of an agent as a creative activity. In its first version, Dev E-R uses an agent that can see and move its head;through interactions with its environment, it is able to develop elaborated behaviors consistent with Piaget's ideas. This work describes an advancement of our model. We give our agent a hand (tactile sensor) so it can detect the presence and features of an object in its environment;we also study the necessary mechanisms to coordinate its vision with the sense of touch. We report the behavior of the agent when it is granted the capacity of touching without seeing (i.e. the agent was blind) and when both skills, touch and sight, come together. For such purpose, we place an agent in a virtual environment and let it perform in different contexts. We analyze how new knowledge structures results from prior experiences and interactions with the environment. The outcomes from the experiments reveal that it learns new skills associated with eye-hand coordination. We observe that the arising developmental behavior resembles some of the features reported by Jean Piaget.

关键词： developmental robotics creative behavior Piaget engagement-reflection Dev E-R eye-hand coordination

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Autonomous robots: potential, advances and future direction

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ELEKTROTECHNIK UND INFORMATIONSTECHNIK 2017年第6期134卷 293-298页

作者： Hangl, S. Ugur, E. Piater, J. Univ Innsbruck Intelligent & Interact Syst Tech Str 21A A-6020 Innsbruck Austria Bogazici Univ Istanbul Turkey

Recent advances in machine learning, such as deep neural networks, have caused a huge boost in many different areas of artificial intelligence and robotics. These methods typically require a large corpus of well-prepared and labelled training data, which limits the applicability to robotics. In our opinion, a fundamental challenge in autonomous robotics is to design systems that are simple enough to solve simple tasks. These systems should grow in complexity step by step and more complex models like neural networks should be trained by re-using the information acquired over the robot's lifetime. Ultimately, high-level abstractions should be generated from these models, bridging the gap from low-level sensor data to high-level AI systems. We present first steps into this direction and analyse their limitations and future extensions in order to achieve the goal of designing autonomous agents.

关键词： autonomous robots cognitive robotics developmental robotics lifelong learning robot creativity robot playing

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BOOTSTRAPPING PERCEPTION USING INFORMATION THEORY: CASE STUDIES IN A QUADRUPED ROBOT RUNNING ON DIFFERENT GROUNDS

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ADVANCES IN COMPLEX SYSTEMS 2013年第2-3期16卷 1-25页

作者： Schmidt, Nico M. Hoffmann, Matej Nakajima, Kohei Pfeifer, Rolf Univ Zurich Dept Informat Artificial Intelligence Lab CH-8050 Zurich Switzerland

Animals and humans engage in an enormous variety of behaviors which are orchestrated through a complex interaction of physical and informational processes: The physical interaction of the bodies with the environment is intimately coupled with informational processes in the animal's brain. A crucial step toward the mastery of all these behaviors seems to be to understand the flows of information in the sensorimotor networks. In this study, we have performed a quantitative analysis in an artificial agent - a running quadruped robot with multiple sensory modalities - using tools from information theory (transfer entropy). Starting from very little prior knowledge, through systematic variation of control signals and environment, we show how the agent can discover the structure of its sensorimotor space, identify proprioceptive and exteroceptive sensory modalities, and acquire a primitive body schema. In summary, we show how the analysis of directed information flows in an agent's sensorimotor networks can be used to bootstrap its perception and development.

关键词： Information theory transfer entropy perception developmental robotics sensorimotor contingencies body schema

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The infant development timeline and its application to robot shaping

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ADAPTIVE BEHAVIOR 2011年第5期19卷 335-358页

作者： Law, James Lee, Mark Huelse, Martin Tomassetti, Alessandra Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Univ Campus Biomed Roma Dipartimento Neurosci Sviluppo Rome Italy

Early infancy is a time of remarkable sensorimotor learning and rapid cognitive growth. Such development offers a rich source of inspiration for models that might allow robotic systems to learn cumulatively and autonomously. This article consists of three parts. The first part introduces the key issues from a robotics perspective. In particular, we promote a constructivist approach in which synthesis provides complementary insights to analytical methods. The second part presents the findings of a detailed review of infant development, from conception to 12 months. This produced explicit timelines that display the relative ordering of emergent competencies and concomitant stages in behavior. The final part shows how this infant framework can be mapped into the robotic domain in order to construct and explore developmental algorithms for robots. This work explores the use of constraints on perception, cognition, and action in shaping emergent activity, and how constraints relate to staged behavior and competence learning. We draw on examples from our previous experiments and show how we are applying this developmental approach to an iCub humanoid robot.

关键词： robotics infant development developmental robotics sensorimotor control

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From deliberative to routine behaviors: A cognitively inspired action-selection mechanism for routine behavior capture

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ADAPTIVE BEHAVIOR 2007年第2期15卷 199-216页

作者： Chernova, Sonia Arkin, Ronald C. Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

Long-term human-robot interaction, especially in the case of humanoid robots, requires an adaptable and varied behavior base. In this paper, we present a method for capturing, or learning, sequential tasks by transferring serial behavior execution from deliberative to routine control. The incorporation of this approach leads to the natural development of complex and varied behaviors, with lower demands for planning, coordination and resources. We demonstrate how this process can be performed autonomously as part of the normal function of the robot, without the need for an explicit learning stage or user guidance. The complete implementation of this algorithm on the Sony QRIO humanoid robot is described.

关键词： action selection developmental robotics routine behavior humanoid robot robot architecture

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Learning to learn

Learning to learn

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6th IEEE International Conference on Development Learning

作者： Butko, Nicholas J. Movellan, Javier R. Univ Calif San Diego Dept Cognit Sci La Jolla CA 92093 USA Univ Calif San Diego Inst Neural Computat La Jolla CA 92093 USA

ISBN: (纸本)9781424411153

Empirical evidence shows that infants 10 months of age can learn about 10 times faster than infants 2 months of age that a novel entity is socially contingent. This suggests that during the period from 2 to 10 months of age infants became better learners. One possible explanation for this change is that new brain structures grow, in a genetically predetermined manner, that support more efficient learning. An analogy for this point of view would be the increase in mastication efficiency due to the growth of teeth. An alternative hypothesis is that the increase in learning efficiency is itself the result of a learning process that operates on the time scale of months. Under this view, better learning is the consequence of learning itself. Here we explore the plausibility of the "learning to learn" hypothesis from a computational point of view. We show that with standard reinforcement learning algorithms using an internally generated reinforcement signal it is possible to develop agents that progressively learn to learn within a period of months. The results fit well at a qualitative level empirical evidence regarding the development of social contingency detection in infants. The learning techniques that we explored have potential application for robots that learn to learn on their own.

关键词： infomax control infomax reinforcement learning (IRL) social contingency temporal dynamics of social interaction Probabilistic functionalism developmental robotics social robotics Probabilistic robotics.

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Grounding semantic categories in behavioral interactions: Experiments with 100 objects

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robotics AND AUTONOMOUS SYSTEMS 2014年第5期62卷 632-645页

作者： Sinapov, Jivko Schenck, Connor Staley, Kerrick Sukhoy, Vladimir Stoytchev, Alexander Iowa State Univ Dev Robot Lab Ames IA 50011 USA

From an early stage in their development, human infants show a profound drive to explore the objects around them. Research in psychology has shown that this exploration is fundamental for learning the names of objects and object categories. To address this problem in robotics, this paper presents a behavior-grounded approach that enables a robot to recognize the semantic labels of objects using its own behavioral interaction with them. To test this method, our robot interacted with 100 different objects grouped according to 20 different object categories. The robot performed 10 different behaviors on them, while using three sensory modalities (vision, proprioception and audio) to detect any perceptual changes. The results show that the robot was able to use multiple sensorimotor contexts in order to recognize a large number of object categories. Furthermore, the category recognition model presented in this paper was able to identify sensorimotor contexts that can be used to detect specific categories. Most importantly, the robot's model was able to reduce exploration time by half by dynamically selecting which exploratory behavior should be applied next when classifying a novel object. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Semantic perception Active and interactive perception Category recognition Learning and adaptive system Behavior-based robotics developmental robotics

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Robots Learning to Say "No": Prohibition and Rejective Mechanisms in Acquisition of Linguistic Negation

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ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 2019年第4期8卷 1–26页

作者： Forster, Frank Saunders, Joe Lehmann, Hagen Nehaniv, Chrystopher L. Univ Hertfordshire Coll Lane Hatfield AL10 9AB Herts England Univ Macerata Ple Luigi Bertelli 1 I-62100 Macerata Italy Univ Waterloo 200 Univ Ave W Waterloo ON N2L 3G1 Canada

"No" is one of the first ten words used by children and embodies the first form of linguistic negation. Despite its early occurrence, the details of its acquisition remain largely unknown. The circumstance that "no" cannot be construed as a label for perceptible objects or events puts it outside the scope of most modern accounts of language acquisition. Moreover, most symbol grounding architectures will struggle to ground the word due to its non-referential character. The presented work extends symbol grounding to encompass affect and motivation. In a study involving the child-like robot iCub, we attempt to illuminate the acquisition process of negation words. The robot is deployed in speech-wise unconstrained interaction with participants acting as its language teachers. The results corroborate the hypothesis that affect or volition plays a pivotal role in the acquisition process. Negation words are prosodically salient within prohibitive utterances and negative intent interpretations such that they can be easily isolated from the teacher's speech signal. These words subsequently may be grounded in negative affective states. However, observations of the nature of prohibition and the temporal relationships between its linguistic and extra-linguistic components raise questions over the suitability of Hebbian-type algorithms for certain types of language grounding.

关键词： developmental robotics language acquisition symbol grounding human-robot interaction

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