Machine Learning and Soft Robotics

Mirza, Nada Masood

doi:10.1109/acit50332.2020.9300102

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Machine learning, renowned for its efficacy in solving nonlinear problems across various fields [168,169], has been increasingly applied to soft robotics. Its applications extend to soft sensor calibration [170,171], soft actuator position control [172,173], and more intricate tasks like grasping and in-hand manipulation [69].…”

Section: Machine Learning In Soft Handsmentioning

confidence: 99%

Anthropomorphic Soft Hand: Dexterity, Sensing, and Machine Learning

Wang,

Hao,

Liu

et al. 2024

Actuators

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Humans possess dexterous hands that surpass those of other animals, enabling them to perform intricate, complex movements. Soft hands, known for their inherent flexibility, aim to replicate the functionality of human hands. This article provides an overview of the development processes and key directions in soft hand evolution. Starting from basic multi-finger grippers, these hands have made significant advancements in the field of robotics. By mimicking the shape, structure, and functionality of human hands, soft hands can partially replicate human-like movements, offering adaptability and operability during grasping tasks. In addition to mimicking human hand structure, advancements in flexible sensor technology enable soft hands to exhibit touch and perceptual capabilities similar to humans, enhancing their performance in complex tasks. Furthermore, integrating machine learning techniques has significantly promoted the advancement of soft hands, making it possible for them to intelligently adapt to a variety of environments and tasks. It is anticipated that these soft hands, designed to mimic human dexterity, will become a focal point in robotic hand development. They hold significant application potential for industrial flexible gripping solutions, medical rehabilitation, household services, and other domains, offering broad market prospects.

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