Machine Learning for Soft Robotics

Abstract

Soft Robotics refers to the design, fabrication, and control of robots made from highly deformable materials that mimic biological organisms. They can perform tasks that require flexibility and soft dexterity. With the application of Machine Learning (ML)， soft robots are now able to learn from data, redesign their shape, and handle various tasks with high precision. This literature review explores the integration of ML techniques in soft robotics, examining various data-driven strategies, applications, knowledge gaps, and future directions. Soft robots often require complex, nonlinear dynamics, and autonomous decision-making capability without explicit programming in order to fit in unpredictable environments.Therefore, despite the significant advances in this field, there are still a few technical challenges topical to various implementations of soft robots. In this literature review, five representative ML methods and their diverse applications for soft robots are analyzed. Lastly, the emerging bio-inspired energy-efficient neuromorphic learning is introduced, which uses far less power than traditional computing technologies and allows for a rapid adaption of soft robots to complex environmental changes. Therefore, neuromorphic learning is regarded as a promising tool for efficient event-driven sensing and adaptive locomotion control in the field of soft robotics.