Eyes-Neck Coordination Using Chaos

Duran, Boris; Kuniyoshi, Yasuo; Sandini, Giulio

doi:10.1007/978-3-540-78317-6_9

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Consequently, humanoid robots often rely on the larger range of whole camera motions to perform gaze stabilization [3,24,21,8,14]. The Cog robotic platform uses a learned mapping between retinal displacement to motor command to compensate for motion sensed from optical flow and vestibular signals [3].…”

Section: Related Workmentioning

confidence: 99%

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Active gaze stabilization

Lesmana¹,

Landgren

Forssén

et al. 2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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We describe a system for active stabilization of cameras mounted on highly dynamic robots. To focus on careful performance evaluation of the stabilization algorithm, we use a camera mounted on a robotic test platform that can have unknown perturbations in the horizontal plane, a commonly occurring scenario in mobile robotics. We show that the camera can be effectively stabilized using an inertial sensor and a single additional motor, without a joint position sensor. The algorithm uses an adaptive controller based on a model of the vertebrate Cerebellum for velocity stabilization, with additional drift correction. We have also developed a resolution adaptive retinal slip algorithm that is robust to motion blur.We evaluated the performance quantitatively using another high speed robot to generate repeatable sequences of large and fast movements that a gaze stabilization system can attempt to counteract. Thanks to the high-accuracy repeatability, we can make a fair comparison of algorithms for gaze stabilization. We show that the resulting system can reduce camera image motion to about one pixel per frame on average even when the platform is rotated at 200 degrees per second. As a practical application, we also demonstrate how the common task of face detection benefits from active gaze stabilization.

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Section: Related Workmentioning

confidence: 99%

“…robot [2] uses a chaotic controller that relies on visual feedback to stabilize the camera motion [8]. The camera stabilization system in [24] incorporates a controller based on the feedback error learning model of the cerebellum as proposed by Kawato [13].…”

Section: Related Workmentioning

confidence: 99%

Active gaze stabilization

Lesmana¹,

Landgren

Forssén

et al. 2014

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

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show abstract

“…The assertion that optimum dynamics should include some factor of variance (i.e., from chaos) is certainly interesting, and apparently also quite practical. Duran et al (2007a , b , 2008 ) showed that it is possible to develop real-time, dynamic smooth pursuit behavior in robotic systems, using a coupled chaotic systems approach. This tracking behavior is readily responsive to known and novel objects, and their motion trajectories.…”

Section: Introductionmentioning

confidence: 99%

Orderliness of Visual Stimulus Motion Mediates Sensorimotor Coordination

Haworth

Stergiou

2018

Front. Physiol.

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We explored the coupling of gaze and postural sway to the motion of a visual stimulus, to further understand sensorimotor coordination. Visual stimuli consisted of a horizontally oscillating red dot, moving with periodic (sine), chaotic, or aperiodic (brown noise) temporal structure. Cross Recurrence Quantification Analysis (cRQA) was used to investigate the coupling between each measured signal with the time series of the visual stimulus position. The cRQA parameter of percent determinism indicated similar strength of coupling of gaze with either periodic or chaotic motion structures, yet weaker coupling to aperiodic stimulus motion. The cRQA parameter of Maxline indicated a particular affinity toward chaotic motion. Analysis of postural coupling supports the idea that the complex periodicity of body sway affords interactivity with non-simple environmental dynamics. These results collectively strengthen the argument that chaos is an invariant and beneficial feature of biological motion, a feature which may be critical for immediate and robust coordination of the self with the environment and other environmental agents.

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Chaotic vs. random coverage missions

Hackbarth

2009

2009 IEEE International Conference on Industrial Engineering and Engineering Management

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Eyes-Neck Coordination Using Chaos

Cited by 4 publications

References 11 publications

Active gaze stabilization

Active gaze stabilization

Orderliness of Visual Stimulus Motion Mediates Sensorimotor Coordination

Chaotic vs. random coverage missions

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