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INTERNATIONAL JOURNAL OF HUMANOID robotics 2008年第2期5卷 161-181页

作者： Hersch, Micha Sauser, Eric Billard, Aude Ecole Polytech Fed Lausanne Sch Engn LASA CH-1015 Lausanne Switzerland

We present an algorithm enabling a humanoid robot to visually learn its body schema, knowing only the number of degrees of freedom in each limb. By "body schema" we mean the joint positions and orientations and thus the kinematic function. The learning is performed by visually observing its end-effectors when moving them. With simulations involving a body schema of more than 20 degrees of freedom, results show that the system is scalable to a high number of degrees of freedom. Real robot experiments confirm the practicality of our approach. Our results illustrate how subjective space representation can develop as a result of sensorimotor contingencies.

关键词： Kinematic learning tool use adaptation body schema peripersonal space representation multimodal integration developmental robotics

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State-Consistency Loss for Learning Spatial Perception Tasks From Partial Labels

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IEEE robotics AND AUTOMATION LETTERS 2021年第2期6卷 1112-1119页

作者： Nava, Mirko Gambardella, Luca Maria Giusti, Alessandro Dalle Molle Inst Artificial Intelligence IDSIA USI SUPSI CH-6928 Lugano Switzerland

When learning models for real-world robot spatial perception tasks, one might have access only to partial labels: this occurs for example in semi-supervised scenarios (in which labels are not available for a subset of the training instances) or in some types of self-supervised robot learning (where the robot autonomously acquires a labeled training set, but only acquires labels for a subset of the output variables in each instance). We introduce a general approach to deal with this class of problems using an auxiliary loss enforcing the expectation that the perceived environment state should not abruptly change;then, we instantiate the approach to solve two robot perception problems: a simulated ground robot learning long-range obstacle mapping as a 400-binary-label classification task in a self-supervised way in a static environment;and a real nano-quadrotor learning human pose estimation as a 3-variable regression task in a semi-supervised way in a dynamic environment. In both cases, our approach yields significant quantitative performance improvements (average increase of 6 AUC percentage points in the former;relative improvement of the R-2 metric ranging from 7% to 33% in the latter) over baselines.

关键词： Robots Task analysis Robot sensing systems Training Sensors Predictive models Robot vision systems Deep learning methods deep learning for visual perception developmental robotics

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Exploration With Intrinsic Motivation Using Object-Action-Outcome Latent Space

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2023年第2期15卷 325-336页

作者： Sener, Melisa Idil Nagai, Yukie Oztop, Erhan Ugur, Emre Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkiye Univ Tokyo Int Res Ctr Neurointelligence Tokyo 1130033 Japan Univ Tokyo Inst AI Tokyo 1130033 Japan Osaka Univ Inst Open & Transdisciplinary Res Initiat Symbiot Intelligent Syst Res Ctr SISReC Suita 5650871 Japan Ozyegin Univ Dept Comp Sci TR-34662 Istanbul Turkiye

One effective approach for equipping artificial agents with sensorimotor skills is to use self-exploration. To do this efficiently is critical, as time and data collection are costly. In this study, we propose an exploration mechanism that blends action, object, and action outcome representations into a latent space, where local regions are formed to host forward model (FM) learning. The agent uses intrinsic motivation to select the FM with the highest learning progress (LP) to adopt at a given exploration step. This parallels how infants learn, as high LP indicates that the learning problem is neither too easy nor too difficult in the selected region. The proposed approach is validated with a simulated robot in a tabletop environment. The simulation scene comprises a robot and various objects, where the robot interacts with one of them each time using a set of parameterized actions and learns the outcomes of these interactions. With the proposed approach, the robot organizes its curriculum of learning as in existing intrinsic motivation approaches and outperforms them in learning speed. Moreover, the learning regime demonstrates features that partially match infant development;in particular, the proposed system learns to predict the outcomes of different skills in a staged manner.

关键词： developmental robotics effect prediction intrinsic motivation (IM) open-ended learning representation learning

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Enhancing a Neurocognitive Shared Visuomotor Model for Object Identification, Localization, and Grasping With Learning From Auxiliary Tasks

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2022年第4期14卷 1331-1343页

作者： Kerzel, Matthias Abawi, Fares Eppe, Manfred Wermter, Stefan Univ Hamburg Dept Informat D-22527 Hamburg Germany

We present a follow-up study on our unified visuomotor neural model for the robotic tasks of identifying, localizing, and grasping a target object in a scene with multiple objects. Our Retinanet-based model enables end-to-end training of visuomotor abilities in a biologically inspired developmental approach. In our initial implementation, a neural model was able to grasp selected objects from a planar surface. We embodied the model on the NICO humanoid robot. In this follow-up study, we expand the task and the model to reaching for objects in a 3-D space with a novel data set based on augmented reality and a simulation environment. We evaluate the influence of training with auxiliary tasks, i.e., if learning of the primary visuomotor task is supported by learning to classify and locate different objects. We show that the proposed visuomotor model can learn to reach for objects in a 3-D space. We analyze the results for biologically plausible biases based on object locations or properties. We show that the primary visuomotor task can be successfully trained simultaneously with one of the two auxiliary tasks. This is enabled by a complex neurocognitive model with shared and task-specific components, similar to models found in biological systems.

关键词： Task analysis Robots Biological system modeling Multitasking Solid modeling Visualization Grasping Bio-inspired robotics Learning systems Cognitive systems Visual systems Bioinspired visuomotor learning cognitive robotics developmental robotics multitask learning

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Learning acceptable windows of contingency

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CONNECTION SCIENCE 2006年第2期18卷 217-228页

作者： Gold, Kevin Scassellati, Brian Yale Univ Dept Comp Sci New Haven CT 06511 USA

By learning a range of possible times over which the effect of an action can take place, a robot can reason more effectively about causal and contingent relationships in the world. An algorithm is presented for learning the interval [t(1min), t(1max)] of possible times during which a response to an action can take place. The algorithm was implemented on a physical robot for the domains of visual self-recognition and auditory social-partner recognition. The environment model assumes that natural environments generate Poisson distributions of random events at all scales. A linear-time algorithm called Poisson threshold learning can generate a threshold T that provides an arbitrarily small rate of background events lambda(T), if such a threshold exists for the specified error rate.

关键词： contingency Poisson threshold learning developmental robotics causality self-recognition social-partner recognition

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The initial development of object knowledge by a learning robot

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robotics AND AUTONOMOUS SYSTEMS 2008年第11期56卷 879-890页

作者： Modayil, Joseph Kuipers, Benjamin Univ Rochester Dept Comp Sci Rochester NY 14627 USA Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

We describe how a robot can develop knowledge of the objects in its environment directly from unsupervised sensorimotor experience. The object knowledge consists of multiple integrated representations: trackers that form spatio-temporal clusters of sensory experience, percepts that represent properties for the tracked objects, classes that support efficient generalization front past experience, and actions that reliably change object percepts, we evaluate how well this intrinsically acquired object knowledge call be used to solve externally specified tasks, including object recognition and achieving goals that require both planning and continuous control. (C) 2008 Elsevier B.V. All rights reserved.

关键词： developmental robotics Object representation Object perception Object actions Machine learning

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Making sense of words: a robotic model for language abstraction

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AUTONOMOUS ROBOTS 2017年第2期41卷 367-383页

作者： Stramandinoli, Francesca Marocco, Davide Cangelosi, Angelo Ist Italiano Tecnol iCub Facil Dept I-16163 Genoa Italy Univ Plymouth Ctr Robot & Neural Syst Plymouth PL4 8AA Devon England

Building robots capable of acting independently in unstructured environments is still a challenging task for roboticists. The capability to comprehend and produce language in a 'human-like' manner represents a powerful tool for the autonomous interaction of robots with human beings, for better understanding situations and exchanging information during the execution of tasks that require cooperation. In this work, we present a robotic model for grounding abstract action words (i.e. USE, MAKE) through the hierarchical organization of terms directly linked to perceptual and motor skills of a humanoid robot. Experimental results have shown that the robot, in response to linguistic commands, is capable of performing the appropriate behaviors on objects. Results obtained in case of inconsistency between the perceptual and linguistic inputs have shown that the robot executes the actions elicited by the seen object.

关键词： developmental robotics Language modeling Sensorimotor knowledge Symbol grounding Embodiment

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A comparison of learning strategies for biologically constrained development of gaze control on an iCub robot

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AUTONOMOUS ROBOTS 2014年第1期37卷 97-110页

作者： Shaw, Patricia Law, James Lee, Mark Aberystwyth Univ Dept Comp Sci Aberystwyth Dyfed Wales

Gaze control requires the coordination of movements of both eyes and head to fixate on a target. We present a biologically constrained architecture for gaze control and show how the relationships between the coupled sensorimotor systems can be learnt autonomously from scratch, allowing for adaptation as the system grows or changes. Infant studies suggest developmental learning strategies, which can be applied to sensorimotor learning in humanoid robots. We examine two strategies (sequential and synchronous) for the learning of eye and head coupled mappings, and give results from implementations on an iCub robot. The results show that the developmental approach can give fast, cumulative, on-line learning of coupled sensorimotor systems.

关键词： developmental robotics Gaze control Sensorimotor learning Eye-head coordination Humanoid robotics

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Interactive object recognition using proprioceptive and auditory feedback

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INTERNATIONAL JOURNAL OF robotics RESEARCH 2011年第10期30卷 1250-1262页

作者： Sinapov, Jivko Bergquist, Taylor Schenck, Connor Ohiri, Ugonna Griffith, Shane Stoytchev, Alexander Iowa State Univ Dev Robot Lab Ames IA 50011 USA

In this paper we propose a method for interactive recognition of household objects using proprioceptive and auditory feedback. In our experiments, the robot observed the changes in its proprioceptive and auditory sensory streams while performing five exploratory behaviors (lift, shake, drop, crush, and push) on 50 common household objects (e.g. bottles, cups, balls, toys, etc.). The robot was tasked with recognizing the objects it was manipulating by feeling them and listening to the sounds that they make without using any visual information. The results show that both proprioception and audio, coupled with exploratory behaviors, can be used successfully for object recognition. Furthermore, the robot was able to integrate feedback from the two modalities, to achieve even better recognition accuracy. Finally, the results show that the robot can boost its recognition rate even further by applying multiple different exploratory behaviors on the object.

关键词： developmental robotics behavior-based systems exploratory behaviors interactive object recognition multimodal object recognition

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Survey and perspective on social emotions in robotics

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ADVANCED robotics 2022年第1-2期36卷 17-32页

作者： Hieida, Chie Nagai, Takayuki Nara Inst Sci & Technol Grad Sch Sci & Technol Div Informat Sci Ikoma Japan Osaka Univ Grad Sch Engn Sci Dept Syst Innovat Suita Osaka Japan Univ Electrocommun Chofu Tokyo Japan

This paper presents a review of available research concerning social emotions in robotics. In robotics, the study of emotions has been pursued since a long time. The popular research endeavors in robotics concern the study of robotic recognition, expression, and computational modeling of the basic mechanisms that underlie them. The advancements in research relevant to this domain are in accordance with well-known psychological findings obtained using the category and dimension theories. Many studies are based on these basic theories, which exclusively address the basic emotions. However, social emotions, also referred to as high-level emotions, have been investigated in psychology. We believe that these high-level emotions are worth investigating for the development of next-generation robotic systems - socially aware robots. This paper summarizes the findings concerning social emotions reported through psychology and neuroscience research endeavors along with a survey of studies concerning social emotions in robotics conducted to date. Moreover, this paper discusses future research directions to facilitate the implementation of social emotions in robots.

关键词： Social emotion emotional robotics emotion model developmental robotics survey paper

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