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184
Intrinsic motivation systems for autonomous mental development
- IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2007
"... Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this dr ..."
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Cited by 255 (56 self)
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Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without
Learning object affordances: From sensory–motor coordination to imitation
- IEEE TRANSACTIONS ON ROBOTICS
, 2008
"... Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the w ..."
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Cited by 85 (7 self)
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Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.
An emergent framework for self-motivation in developmental robotics
- in Proceedings of the 3rd International Conference on Development and Learning (ICDL 2004), Salk Institute
, 2004
"... This paper explores a philosophy and connectionist algorithm for creating a long-term, self-motivated developmental robot control system. Self-motivation is viewed as an emergent property arising from two competing pressures: the need to accurately predict the environment while simultaneously wantin ..."
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Cited by 50 (2 self)
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This paper explores a philosophy and connectionist algorithm for creating a long-term, self-motivated developmental robot control system. Self-motivation is viewed as an emergent property arising from two competing pressures: the need to accurately predict the environment while simultaneously wanting to seek out novelty in the environment. These competing internal pressures are designed to drive the system in a manner reminiscent of a co-evolutionary arms race. 1
R-IAC: Robust Intrinsically Motivated Exploration and Active Learning
- IEEE Transactions on Autonomous Mental Development
, 2009
"... Abstract—Intelligent adaptive curiosity (IAC) was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without preprogramming the particular developmental stages. In this paper, we argue that IAC and other intrinsical ..."
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Cited by 47 (12 self)
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Abstract—Intelligent adaptive curiosity (IAC) was initially introduced as a developmental mechanism allowing a robot to self-organize developmental trajectories of increasing complexity without preprogramming the particular developmental stages. In this paper, we argue that IAC and other intrinsically motivated learning heuristics could be viewed as active learning algorithms that are particularly suited for learning forward models in un-prepared sensorimotor spaces with large unlearnable subspaces. Then, we introduce a novel formulation of IAC, called robust intelligent adaptive curiosity (R-IAC), and show that its perfor-mances as an intrinsically motivated active learning algorithm are far superior to IAC in a complex sensorimotor space where only a small subspace is neither unlearnable nor trivial. We also show results in which the learnt forward model is reused in a control scheme. Finally, an open source accompanying software containing these algorithms as well as tools to reproduce all the experiments presented in this paper is made publicly available. Index Terms—Active learning, artificial curiosity, developmental robotics, exploration, intrinsic motivation, sensorimotor learning.
The playground experiment: Task-independent development of a curious robot
- Proceedings of the AAAI Spring Symposium on Developmental Robotics, 2005, Pages
, 2005
"... This paper presents the mechanism of Intelligent Adaptive Curiosity. This is an intrinsic motivation system which pushes the robot towards situations in which it maximizes its learning progress. It makes the robot focus on situations which are neither too predictable nor too unpredictable. This mech ..."
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Cited by 47 (11 self)
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This paper presents the mechanism of Intelligent Adaptive Curiosity. This is an intrinsic motivation system which pushes the robot towards situations in which it maximizes its learning progress. It makes the robot focus on situations which are neither too predictable nor too unpredictable. This mechanism is a source of autonomous mental development for the robot: the complexity of its activities autonomously increases and a developmental sequence appears without being manually constructed. We test this motivation system on a real robot which evolves on a baby play mat with objects that it can learn to manipulate. We show that it first spends time in situations which are easy to learn, then shifts progressively its attention to situations of increasing
From unknown sensors and actuators to actions grounded in sensorimotor perceptions
- Connection Science
, 2006
"... This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory input and the actuators, and the system relies o ..."
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Cited by 45 (5 self)
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This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory input and the actuators, and the system relies on generic properties of the robot’s world such as piecewise smooth effects of movement on sensory changes. The robot develops the model of its sensorimotor system by first performing random movements to create an informational map of the sensors. Using this map the robot then learns what effects the different possible actions have on the sensors. After this developmental process the robot can perform basic visually guided movement.
Predictive information and explorative behavior of autonomous robots
- The European Physical Journal B
, 2008
"... SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for pap ..."
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Cited by 43 (11 self)
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SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited visit to or collaboration at SFI, or funded by an SFI grant. ©NOTICE: This working paper is included by permission of the contributing author(s) as a means to ensure timely distribution of the scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the author(s). It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may be reposted only with the explicit permission of the copyright holder. www.santafe.edu SANTA FE INSTITUTE Predictive information and explorative behavior
Bringing up robot: Fundamental mechanisms for creating a self-motivated, self-organizing architecture
- Cybernetics and Systems
, 2005
"... Under review for a special issue of the journal Cybernetics and Systems ..."
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Cited by 38 (6 self)
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Under review for a special issue of the journal Cybernetics and Systems
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
, 2010
"... This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning ..."
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Cited by 32 (10 self)
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This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.
Ongoing emergence: A core concept in epigenetic robotics
- In EpiRob
, 2005
"... We propose ongoing emergence as a core concept in epigenetic robotics. Ongoing emergence refers to the continuous development and integration of new skills and is exhibited when six criteria are satisfied: (1) continuous skill acquisition, (2) incorporation of new skills with existing skills, (3) au ..."
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Cited by 22 (2 self)
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We propose ongoing emergence as a core concept in epigenetic robotics. Ongoing emergence refers to the continuous development and integration of new skills and is exhibited when six criteria are satisfied: (1) continuous skill acquisition, (2) incorporation of new skills with existing skills, (3) autonomous development of values and goals, (4) bootstrapping of initial skills, (5) stability of skills, and (6) reproducibility. In this paper we: (a) provide a conceptual synthesis of ongoing emergence based on previous theorizing, (b) review current research in epigenetic robotics in light of ongoing emergence, (c) provide prototypical examples of ongoing emergence from infant development, and (d) outline computational issues relevant to creating robots exhibiting ongoing emergence. 1.
