Our lab leads the Brain group in the ERC Soft Robotics Research Center at Seoul National University. We facilitate machine learning approaches in applications to solve problems regarding soft robotics, especially, control and situation awareness of flexible robots and soft wearable robots.
For stroke survivors, diminished hand functions limit their ability to perform Activities of Daily Living (ADLs). To help stroke survivors perform rehabilitation exercises, soft robotic gloves have been utilized to assist them in actively practicing Read more…
We report a substrate-less nanomesh receptor that is coupled with an unsupervised meta-learning framework and can provide user-independent, data-efficient recognition of different hand tasks. The nanomesh, which is made from biocompatible materials and can be Read more…
Soft wearable robotic gloves based on tendon-sheath mechanism are widely developed for assisting people with a loss of hand mobility. For these robots, knowing the fingertip forces applied to deformable objects is crucial in successfully Read more…
While data-driven approaches could model complex sensor patterns, the required amount of labeled data increases exponentially as the number of contact points or the number of sensor subelements increase. We propose a novel deep learning Read more…
We develop an ultra-sensitive skin-like sensor that discovers previously undetectable signals far from the joint, assisted by a deep neural network that deciphers patterns within the signals. By utilizing a single sensor capable of extracting Read more…
Soft wearable hand robots with tendon-sheath mechanisms are being actively developed to assist people with lost hand mobility. For these robots, accurately estimating fingertip forces leads to successful object grasping. An approach can utilize information Read more…
Developing Soft Robotics To Assist Users With Hand-Object Interactions [의도 파악하는 웨어러블 손 로봇 관련 기사들] For hand robots that assist with activities of daily living, this study presents a paradigm for learning-based intention Read more…
We present a gait motion generating method using only two microfluidic sensors. We select appropriate sensor positions with consideration of the deformation patterns of the lower-limb skins and mutual interference with soft actuators. A Read more…
We propose use of deep learning for full-body motion sensing, which significantly increases efficiency in calibration and estimation. The sensing suit is made of stretchable fabric and contains 20 soft strain sensors distributed on Read more…
Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, Read more…