Autonomous online generation of a motor representation of the workspace for intelligent whole-body reaching

Jamone, Lorenzo; Brandão, Martim; Natale, Lorenzo; Hashimoto, Kenji; Sandini, Giulio; Takanishi, Atsuo

doi:10.1016/j.robot.2013.12.011

Cited by 21 publications

(22 citation statements)

References 40 publications

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“…The visual representation of space is maintained by a spherical-like coordinate system that is implicitly defined by the gaze direction. This representation is particularly suitable for autonomous learning and has been adopted in several recent works (Schenck et al, 2003;Hoffmann et al, 2005;Chinellato et al, 2011;Jamone et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Learning the visual–oculomotor transformation: Effects on saccade control and space representation

Antonelli

Durán

Chinellato

et al. 2015

Robotics and Autonomous Systems

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Active eye movements can be exploited to build a visuomotor representation of the surrounding environment. Maintaining and improving such representation requires to update the internal model involved in the generation of eye movements. From this perspective, action and perception are thus tightly coupled and interdependent. In this work, we encoded the internal model for oculomotor control with an adaptive filter inspired by the functionality of the cerebellum. Recurrent loops between a feed-back controller and the internal model allow our system to perform accurate binocular saccades and create an implicit representation of the nearby space. Simulations results show that this recurrent architecture outperforms classical feedback-error-learning in terms of both accuracy and sensitivity to system parameters. The proposed approach was validated implementing the framework on an anthropomorphic robotic head.

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Section: Discussionmentioning

confidence: 99%

Learning the visual–oculomotor transformation: Effects on saccade control and space representation

Antonelli

Durán

Chinellato

et al. 2015

Robotics and Autonomous Systems

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“…In addition, the LWPR networks are widely used to implement robotic internal representations, e.g. [3], [43], [44]. …”

Section: Constructive Neural Network For Incremental Learningmentioning

confidence: 99%

Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

Chao

Zhu

Lin

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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Abstract-Robotic hand-eye coordination is recognized as an important skill to deal with complex real environments. Conventional robotic hand-eye coordination methods merely transfer stimulus signals from robotic visual space to hand actuator space. This paper introduces a reverse method: Build another channel that transfers stimulus signals from robotic hand space to visual space. Based on the reverse channel, a human-like behavior pattern: "Stop-to-Fixate", is imparted to the robot, thereby giving the robot an enhanced reaching ability. A visual processing system inspired by the human retina structure is used to compress visual information so as to reduce the robot's learning complexity. In addition, two constructive neural networks establish the two sensory delivery channels. The experimental results demonstrate that the robotic system gradually obtains a reaching ability. In particular, when the robotic hand touches an unseen object, the reverse channel successfully drives the visual system to notice the unseen object.Index Terms-Robotic hand-eye coordination, sensory motor mapping, human-like behavioral pattern, constructive neural network.

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“…2.1. Section 2.2 evaluates six existing system architectures identified from a review of 32 articles from the human-robot interaction literature [1,2,4,7,8,10,19,24,26,[28][29][30][31][32][33][34][37][38][39][40]44,46,49,50,53,57,58,[61][62][63]67,70] to determine if they can be characterized as a reference architecture. These 32 articles capture atleast.…”

Section: Related Workmentioning

confidence: 99%

“…Speakers tend to look at listeners more when they intend to be more persuasive, deceptive, ingratiating, or assertive [36,49]. Additionally, gestures such as head nods and head shakes have been found to be used for showing mental states, such as emotions [18] or intention [34,53]. The convey general liveliness and awareness function is employed for idle looking behaviors, i.e., when there are no tasks at hand or to interrupt tasks.…”

Section: Metrics For Coverage In the Reference Architecture Of Socialmentioning

confidence: 99%

“…The six system architectures described are-Linta-III [32], extension of the C5M architecture [10], an architecture to initiate and maintain engagement [62], an architecture for multi-modal interaction [7], the Human-Robot Collaboration architecture [26], and the Robot Behavior Toolkit architecture [28][29][30] [49,50], Attentiveness of the Human to the Robot [37,62], Perceived Intelligence [49,50], Empa- [32] Linta III X X [10] Extension of the C5M architecture X X X X [7] Architecture for multi-modal interaction X X X [62] Architecture to initiate and maintain engagement X X X [26] Human-Robot Collaboration Architecture X X X [28][29][30] Robot Behavior Toolkit Architecture X X X LIH look interested in human(s), EVCH engage in verbal conversation with human(s), GOE gaze at objects in the environment, SVMS show various mental states, and CGLA convey general liveliness and awareness thy [50,57], Groupness [50], and Positive Emotional State [50,62]. Five out of the six architectures-Linta-III [32], architecture to initiate and maintain engagement [62], architecture for multi-modal interaction [7], the Human-Robot Collaboration architecture [26], and the Robot Behavior Toolkit architecture [28][29][30] [24,34,53], and Social Presence [24].…”

Section: Identify System Architectures For Head Gaze Generationmentioning

confidence: 99%

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A Reference Architecture for Social Head Gaze Generation in Social Robotics

Srinivasan

Murphy

Bethel

2015

Int J of Soc Robotics

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This article outlines a reference architecture for social head gaze generation in social robots. The architecture discussed here is grounded in human communication, based on behavioral robotics theory, and captures the commonalities, essence, and experience of 32 previous social robotics implementations of social head gaze. No such architecture currently exists, but such an architecture is needed to: (1) serve as a template for creating or re-engineering systems, (2) provide analyses and understanding of different systems, and (3) provide a common lexicon and taxonomy that facilitates communication across various communities. A constructed reference architecture and the Software Architecture Analysis Method (SAAM) are used to evaluate, improve, and re-engineer two existing head gaze system architectures (Human-Robot Collaboration architecture and Robot Behavior Toolkit architecture). SAAM shows that no existing architecture incorporated the summation of functionalities found in the 32 studies. SAAM suggests several architectural improvements so that the two existing architectures can better support adaptation to a new environment and extension of capability. The resulting reference architecture guides the implementation of social head gaze in a rescue robot for the purpose of victim management in urban B Vasant Srinivasan search and rescue (US&R). Using the proposed reference architecture will benefit social robotics because it will simplify the principled implementations of head gaze generation and allow for comparisons between such implementations.

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Autonomous online generation of a motor representation of the workspace for intelligent whole-body reaching

Cited by 21 publications

References 40 publications

Learning the visual–oculomotor transformation: Effects on saccade control and space representation

Learning the visual–oculomotor transformation: Effects on saccade control and space representation

Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

A Reference Architecture for Social Head Gaze Generation in Social Robotics

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