Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

To perceive user intentions for wearable robots, we present a learning-based intention detection methodology using a first-person-view camera.

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In this study, we address an exploration problem when constructing complete 3D models in an unknown environment using a Micro-Aerial Vehicle. Most previous exploration methods were based on the Next-Best-View (NBV) approaches, which iteratively determine the most informative view, that exposes the greatest unknown area from the current partial model. However, these approaches sometimes miss minor unreconstructed regions like holes or sparse surfaces (while these can be important features). Furthermore, because the NBV methods iterate the next-best path from a current partial view, they sometimes produce unnecessarily long trajectories by revisiting known regions. To address these problems, we propose a novel exploration algorithm that integrates coverage and inspection strategies. The suggested algorithm first computes a global plan to cover unexplored regions to complete the target model sequentially. It then plans local inspection paths that comprehensively scans local frontiers. This approach reduces the total exploration time and improves the completeness of the reconstructed models. We evaluate the proposed algorithm in comparison with other state-of-the-art approaches through simulated and real-world experiments. The results show that our algorithm outperforms the other approaches and in particular improves the completeness of surface coverage. IntroductionReconstructed 3D models of large environments are becoming more useful in many industrial fields, including agriculture, engineering, and construction. With the development of various mobile robots, many studies suggest various methods to realize autonomous modeling systems (Blaer and Allen 2009;Ramanagopal et al. 2018;Roberts et al. 2017). Recently, because of rapid technological advances, Micro-Aerial Vehicles (MAVs) have become the most widely-used robots in the modeling systems. With their high maneuverability, MAVs can acquire information from almost any Electronic supplementary material The online version of this article (

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