Human-like robotic handwriting and drawing

Li, Boren; Zheng, Yuan F.; Hemami, H.; Da, Che

doi:10.1109/icra.2013.6631283

Cited by 10 publications

(12 citation statements)

References 23 publications

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“…Smoothness of motion and fast stop are implemented by spinal reflexes. Similar strategies of control are cited in the robotic field [38] and for anthropomorphic robots [49]. A number of hypotheses [30] [50], experimentally-based observations [51], and animal observations [52]- [54] support the model presented here.…”

Section: Introductionsupporting

confidence: 63%

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Neural and Spinal Modules in Implementation of a Simple Ballistic Movement

Hemami¹,

Dariush²

2016

JSEA

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A simple ballistic movement and two of its attributes (namely, reversal in time and synchronization with external events) are formulated. A three-dimensional, three-link musculoskeletal arm is subjected to a fast ballistic type movement. The central components of the movement from hippocampal, cerebellar, basal ganglia and reticular formation structures that may be involved in timing are identified. The role of agonist muscles and spinal reflexes in the execution of ballistic movements (namely, in fast starts and fast stops) is discussed. The needed three time intervals are constructed in real time and can be coordinated with external events. Delaying or advancing in time, synchronization, time scaling and inverting events in time relative to the movement is formulated. Digital computer simulations are presented to test the behavior of the formulated neural and spinal processing and demonstrate the behavior of the arm under such control.

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Section: Introductionsupporting

confidence: 63%

“…The three-link arm is equipped with 15 pairs of muscles and has already been used in some upper extremity research [36]- [38]. A virtual neuromusculoskeletal system is considered in [39].…”

Section: Introductionmentioning

confidence: 99%

Neural and Spinal Modules in Implementation of a Simple Ballistic Movement

Hemami¹,

Dariush²

2016

JSEA

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“…The latter areas of research attract particular attention for their originality and vast unexplored future work. For instance, Li et al [64] introduced a trajectory planning method that can test different hypotheses concerning the role of the human Central Nervous System (CNS) in handwriting and drawing. Human actions seem to be represented in the CNS in more abstractive terms than with specific commands to the muscles [65].…”

Section: Classification Of the Generated Human-like Arm Movementsmentioning

confidence: 99%

“…Human actions seem to be represented in the CNS in more abstractive terms than with specific commands to the muscles [65]. For this reason, handwriting and drawing are encoded in terms of strokes in positions and directions without any specific motor command [64]. Specifically, three methods for imitating human handwriting and drawing are described: basic strokes, Bezier curves and non-gradient numerical optimization methods.…”

Section: Classification Of the Generated Human-like Arm Movementsmentioning

confidence: 99%

“…The time parametrization of the resulting path is adaptable to different velocities profiles that can also simulate neurophysiological injuries and diseases. Concerning the Bezier curve method, Li et al [64] took inspiration from computer graphics for the definition of a particular parametric curve, i.e. the cubic Bezier.…”

Section: Classification Of the Generated Human-like Arm Movementsmentioning

confidence: 99%

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Human-Like Arm Motion Generation: A Review

Gulletta¹,

Erlhagen²,

Bicho³

2020

Preprint

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In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analysed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioural and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

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