9,938 research outputs found
Beyond Gazing, Pointing, and Reaching: A Survey of Developmental Robotics
Developmental robotics is an emerging field located
at the intersection of developmental psychology
and robotics, that has lately attracted
quite some attention. This paper gives a survey of
a variety of research projects dealing with or inspired
by developmental issues, and outlines possible
future directions
Evolution of Prehension Ability in an Anthropomorphic Neurorobotic Arm
In this paper we show how a simulated anthropomorphic robotic arm controlled by an artificial neural network can develop effective reaching and grasping behaviour through a trial and error process in which the free parameters encode the control rules which regulate the fine-grained interaction between the robot and the environment and variations of the free parameters are retained or discarded on the basis of their effects at the level of the global behaviour exhibited by the robot situated in the environment. The obtained results demonstrate how the proposed methodology allows the robot to produce effective behaviours thanks to its ability to exploit the morphological properties of the robotâs body (i.e. its anthropomorphic shape, the elastic properties of its muscle-like actuators, and the compliance of its actuated joints) and the properties which arise from the physical interaction between the robot and the environment mediated by appropriate control rules
Ongoing Emergence: A Core Concept in Epigenetic Robotics
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
Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
One of the open challenges in designing robots that operate successfully in
the unpredictable human environment is how to make them able to predict what
actions they can perform on objects, and what their effects will be, i.e., the
ability to perceive object affordances. Since modeling all the possible world
interactions is unfeasible, learning from experience is required, posing the
challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own
hands (or similar end-effectors), but in some cases the use of tools may be
desirable, nevertheless, it is reasonable to assume that while a robot can
collect many sensorimotor experiences using its own hands, this cannot happen
for all possible human-made tools.
Therefore, in this paper we investigate the developmental transition from
hand to tool affordances: what sensorimotor skills that a robot has acquired
with its bare hands can be employed for tool use? By employing a visual and
motor imagination mechanism to represent different hand postures compactly, we
propose a probabilistic model to learn hand affordances, and we show how this
model can generalize to estimate the affordances of previously unseen tools,
ultimately supporting planning, decision-making and tool selection tasks in
humanoid robots. We present experimental results with the iCub humanoid robot,
and we publicly release the collected sensorimotor data in the form of a hand
posture affordances dataset.Comment: dataset available at htts://vislab.isr.tecnico.ulisboa.pt/, IEEE
International Conference on Development and Learning and on Epigenetic
Robotics (ICDL-EpiRob 2017
The RobotCub Approach to the Development of Cognition
This paper elaborates on the workplan of an
initiative in embodied cognition: RobotCub. Our
goal here is to provide background and to
motivate our long-term plan of empirical
research including brain and robotic sciences
following the principles of epigenetic robotics
Social Situatedness: Vygotsky and Beyond
The concept of âsocial situatednessâ, i.e. the idea that the development of individual intelligence requires a social (and cultural) embedding, has recently received much attention in cognitive science and artificial intelligence research. The work of Lev Vygotsky who put forward this view already in the 1920s has influenced the discussion to some degree, but still remains far from well known. This paper therefore aims to give an overview of his cognitive development theory and discuss its relation to more recent work in primatology and socially situated artificial intelligence, in particular humanoid robotics
An Open-Source Simulator for Cognitive Robotics Research: The Prototype of the iCub Humanoid Robot Simulator
This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the âRobotCubâ project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project âITALKâ on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
âThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." âCopyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.âThis 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: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) 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.Peer reviewe
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Emerging Linguistic Functions in Early Infancy
This paper presents results from experimental
studies on early language acquisition in infants and
attempts to interpret the experimental results within
the framework of the Ecological Theory of
Language Acquisition (ETLA) recently proposed
by (Lacerda et al., 2004a). From this perspective,
the infantâs first steps in the acquisition of the
ambient language are seen as a consequence of the
infantâs general capacity to represent sensory input
and the infantâs interaction with other actors in its
immediate ecological environment. On the basis of
available experimental evidence, it will be argued
that ETLA offers a productive alternative to
traditional descriptive views of the language
acquisition process by presenting an operative
model of how early linguistic function may emerge
through interaction
- âŠ