3/28/2024 0 Comments Machine learning robotcsIt aims to present an overview, analyze the current trend, and discuss current limitations of machine learning algorithms in soft robotics. This paper presents and analyzes existing machine learning methods in the soft robotics. Based on the studies, the use of machine learning-based methods has successfully addressed the current limitations of soft robots. In particular, the applications include soft sensor calibrations, positioning control of soft actuators, and more complex tasks, such as grasping or motion planning of robots. ![]() It is well known that machine learning algorithms are effective in solving non-linear problems in various fields, and they have recently been used to solve problems related to soft robots. These make it difficult to mathematically model soft grippers and calibrate soft sensors, limiting the applications of soft robotics.Ī potential solution to the aforementioned drawbacks is implementation of machine learning techniques. There are additional drawbacks, which include creep, drift, and high degrees-of-freedom (DOF) that increase hysteresis thus contributing to the complexity of the robot behaviors. Hysteresis can be defined as a time-dependent behavior typically shown as an output discrepancy during loading and unloading cycles. Non-linearity indicates that the relationship between the system input and the output cannot be represented by a simple linear relationship. In spite of the advantages of soft robots, there exist common limitations in modeling, calibration, or control since the structural compliance and the viscoelasticity in the material results in complex and unpredictable behaviors due to non-linearity and hysteresis. Several studies combined soft sensors and soft actuators to perform complex tasks like robot perception. Moreover, they are often worn on human bodies for human-robot interactions to enable safe and comfortable assistance and interaction due to their compliant structures. ![]() Examples of their applications include soft grippers for handling fragile or delicate objects and mechanoreceptive or proprioceptive sensing for robot using soft sensors. These robots have advantages over robots made of rigid materials due to their flexibility, compliance, and adaptability to the surrounding environments. Soft robots have been extensively researched with respect to various research fields.
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