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1st IEEE International Conference on Human-Machine Systems (ICHMS)

作者： Houbre, Quentin Angleraud, Alexandre Pieters, Roel Tampere Univ Dept Automat Technol & Mech Engn Cognit Robot Tampere 33720 Finland

ISBN: (纸本)9781728158716

The modelling of cognition is fundamental to designing robots that are increasingly more autonomous. Indeed, researchers take inspiration from human and animal cognition in order to endow robots with the ability to learn and adapt to their environment. In specific cases, the robot has to find the right compromise between exploring the environment, or exploiting its own experience to advance its knowledge of a skill. Our approach considers a neurally-inspired model to learning sensorimotor contingencies based on exploration and exploitation. For the exploration, an inhibition of return mechanism is implemented that generates new actions. In this work, we investigate how the tuning of the inhibition of return affects the exploratory behavior. To do so, we set up an experiment where a 3D printed humanoid robot arm GummiArm has to learn how to move a baby mobile toy with only a visual feedback. The results demonstrate that the tuning of the inhibition of return influences the exploratory behavior, leading to a faster learning of sensorimotor contingencies as well as the exploration of a reduced motor space.

关键词： developmental robotics Cognitive robotics Embodiment Exploration Dynamic Neural Fields

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A Sensorimotor Learning Framework for Object Categorization

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2016年第1期8卷 15-25页

作者： Hogman, Virgile Bjorkman, Marten Maki, Atsuto Kragic, Danica KTH Royal Inst Technol CSC Ctr Autonomous Syst Comp Vision & Act Percept Lab Stockholm Sweden

This paper presents a framework that enables a robot to discover various object categories through interaction. The categories are described using action-effect relations, i.e., sensorimotor contingencies rather than more static shape or appearance representation. The framework provides a functionality to classify objects and the resulting categories, associating a class with a specific module. We demonstrate the performance of the framework by studying a pushing behavior in robots, encoding the sensorimotor contingencies and their predictability with Gaussian Processes. We show how entropy-based action selection can improve object classification and how functional categories emerge from the similarities of effects observed among the objects. We also show how a multidimensional action space can be realized by parameterizing pushing using both position and velocity.

关键词： Active perception categorization cognitive robotics developmental robotics embodiment learning and adaptive system object classification sensorimotor learning

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Detecting the Functional Similarities Between Tools Using a Hierarchical Representation of Outcomes

Detecting the Functional Similarities Between Tools Using a ...

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7th IEEE International Conference on Development and Learning

作者： Sinapov, Jivko Stoytchev, Alexadner Iowa State Univ Dev Robot Lab Ames IA 50011 USA

ISBN: (纸本)9781424426614

The ability to reason about multiple tools and their functional similarities is a prerequisite for intelligent tool use. This paper presents a model which allows a robot to detect the similarity between tools based on the environmental outcomes observed with each tool. To do this, the robot incrementally learns an adaptive hierarchical representation (i.e., a taxonomy) for the types of environmental changes that it can induce and detect with each tool. Using the learned taxonomies, the robot can infer the similarity between different tools based on the types of outcomes they produce. The results show that the robot is able to learn accurate outcome models for six different tools. In addition, the robot was able to detect the similarity between tools using the learned outcome models.

关键词： developmental robotics Autonomous Tool Use Robot Manipulation

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Shaping Robotic Minds

Shaping Robotic Minds

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Conference on Robo-Philosophy - Sociable robotics and the Future of Social Relations

作者： Fernandez Castro, Victor Univ Granada Granada Spain

ISBN: (纸本)9781614994800;9781614994794

Social cognition research has focused on the debate on the nature of mechanisms underlying social abilities. However, the competing views in the debate share a basic assumption: mental states attribution is central for social cognition. The aim of this paper is twofold: firstly, I present an alternative framework known as mindshaping. According to it, human beings are biologically predisposed to learn and teach cultural and rational norms and complex cultural patterns of behaviour that enhance social cognition. Secondly I will highlight how this new framework can open new perspectives of research in the area of social robotics.

关键词： social cognition mindshaping natural pedagogy developmental robotics companion robots

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Learning and generalization of behavior-grounded tool affordances

Learning and generalization of behavior-grounded tool afford...

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6th IEEE International Conference on Development Learning

作者： Sinapov, Jivko Stoytchev, Alexander Iowa State Univ Dept Comp Sci Ames IA USA

ISBN: (纸本)9781424411153

This paper describes an approach which a robot can use to learn the effects of its actions with a tool, as well as identify which frames of reference are useful for predicting these effects. The robot learns the tool representation during a behavioral babbling stage in which it randomly explores the space of its actions and perceives their effects. The experimental results show that the robot is able to learn a compact and accurate model of how its tool actions would affect the position of a target object. Furthermore, the model learned by the robot can generalize and perform well even with tools that the robot has never seen before. Experiments were conducted in a dynamics robot simulator. Two different learning algorithms and five different frames of reference were evaluated based on their generalization performance.

关键词： developmental robotics affordances learning of affordances tool affordances

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Towards Deep developmental Learning

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2016年第2期8卷 99-114页

作者： Sigaud, Olivier Droniou, Alain UPMC Univ Paris 06 Sorbonne Univ F-75005 Paris France CNRS Inst Syst Intelligents & Robot UMR7222 Paris France

Deep learning techniques are having an undeniable impact on general pattern recognition issues. In this paper, from a developmental robotics perspective, we scrutinize deep learning techniques under the light of their capability to construct a hierarchy of meaningful multimodal representations from the raw sensors of robots. These investigations reveal the differences between the methodological constraints of pattern recognition and those of developmental robotics. In particular, we outline the necessity to rely on unsupervised rather than supervised learning methods and we highlight the need for progress towards the implementation of hierarchical predictive processing capabilities. Based on these new tools, we outline the emergence of a new domain that we call deep developmental learning.

关键词： Affordances deep learning developmental robotics hierarchical predictive processing sensorimotor contingencies

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Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills

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ROBOTICA 2015年第5期33卷 1163-1180页

作者： Ugur, Emre Nagai, Yukie Celikkanat, Hande Oztop, Erhan Univ Innsbruck Inst Comp Sci A-6020 Innsbruck Austria Adv Telecommun Res Inst Kyoto Japan Osaka Univ Grad Sch Engn Osaka Japan Middle E Tech Univ Dept Comp Engn Ankara Turkey Ozyegin Univ Dept Comp Sci Istanbul Turkey

Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop earlier than they would in isolation due to caregivers' support. Parents are known to make modifications in infant-directed actions, which are often called "motionese". 7 The features that might be associated with motionese are amplification, repetition and simplification in caregivers' movements, which are often accompanied by increased social signalling. In this paper, we extend our previously developed affordances learning framework to enable our hand-arm robot equipped with a range camera to benefit from parental scaffolding and motionese. We first present our results on how parental scaffolding can be used to guide the robot learning and to modify its crude action execution to speed up the learning of complex skills. For this purpose, an interactive human caregiver-infant scenario was realized with our robotic setup. This setup allowed the caregiver's modification of the ongoing reach and grasp movement of the robot via physical interaction. This enabled the caregiver to make the robot grasp the target object, which in turn could be used by the robot to learn the grasping skill. In addition to this, we also show how parental scaffolding can be used in speeding up imitation learning. We present the details of our work that takes the robot beyond simple goal-level imitation, making it a better imitator with the help of motionese.

关键词： developmental robotics Affordance Imitation Parental scaffolding Motionese

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From Manipulation to Communicative Gesture

From Manipulation to Communicative Gesture

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5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Ou, Shichao Grupen, Rod Univ Massachusetts Lab Perceptual Robot Dept Comp Sci Amherst MA 01003 USA

ISBN: (纸本)9781424448937

This paper advocates an approach for learning communicative actions and manual skills in the same framework. We exploit a fundamental relationship between the structure of motor skills, intention, and communication. Communicative actions are acquired using the same learning framework and the same primitive states and actions that the robot uses to construct manual behavior for interacting with other objects in the environment. A prospective behavior algorithm is used to acquire modular policies for conveying intention and goals to nearby human beings and recruiting their assistance. The learning framework and a preliminary case study are presented in which a humanoid robot learns expressive communicative behavior incrementally by discovering the manual affordances of human beings. Results from interactions with 16 people provide support for the hypothesized benefits of this approach. Behavior reuse makes learning from relatively few interactions possible. This approach compliments other efforts in the field by grounding social behavior, and proposes a mechanism for negotiating a communicative vocabulary between humans and robots.

关键词： communicative behavior learning knowledge acquisition developmental robotics human-robot interaction

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Surprise-Based developmental Learning and Experimental Results on Robots

Surprise-Based Developmental Learning and Experimental Resul...

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8th IEEE International Conference on Development and Learning

作者： Ranasinghe, Nadeesha Shen, Wei-Min Univ So Calif Inst Informat Sci Marina Del Rey CA 90292 USA

ISBN: (纸本)9781424441174

Learning from surprises and unexpected situations is a capability that is critical for developmental learning. This paper describes a promising approach in which a learner robot engages in a cyclic learning process consisting of "prediction, action, observation, analysis (of surprise) and adaptation". In particular, the robot always predicts the consequences of its actions, detects surprises whenever there is a significant discrepancy between the prediction and the observed reality, analyzes the surprises for causes, and uses the analyzed knowledge to adapt to the unexpected situations. We tested this approach on a modular robot learning how to navigate and recover from unexpected changes in sensors, actions, goals, and environments. The results are very encouraging.

关键词： Learning systems unsupervised learning surprise-based learning developmental robotics

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Learning while Sleeping: Integrating Sleep-Inspired Consolidation with Human Feedback Learning

Learning while Sleeping: Integrating Sleep-Inspired Consolid...

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IEEE International Conference on Development and Learning (ICDL)

作者： Tarakli, Imene Di Nuovo, Alessandro Sheffield Hallam Univ Sheffield S Yorkshire England

ISBN: (纸本)9798350348569;9798350348552

Sleep plays a vital role in developmental learning. It allows the brain to consolidate daily learning experiences by replaying the memories accumulated throughout the day. In this work, we take inspiration from sleep and propose the Inverse Forward Offline Reinforcement Model (INFORM), a novel scalable framework that first learns a set of behaviours from human evaluative feedback, then consolidates the learning by applying an offline inverse reinforcement learning to the memorised trajectories. Experimental results demonstrate that INFORM is a feedback-efficient method that effectively learns an optimal policy that aligns with the intended behaviour of the human. A comparative analysis shows that the learnt policies are robust to dynamic changes in the environment and the recovered rewards allow the robot to be autonomous in its learning. Project website: https://***/view/inform-framework

关键词： developmental robotics Cognitive robotics Interactive Agents

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