Electronic skins and machine learning for intelligent soft robots
By Benjamin Shih, Dylan S. Shah, Jinxing Li, Thomas George Thuruthel, Yong‐Lae Park, Fumiya Iida, Zhenan Bao, Rebecca Kramer‐Bottiglio, Michael Thomas Tolley. Science Robotics, 2020
642 citations recorded by the source corpus.
Developments in e-skins and machine learning may achieve tactile sensing and proprioception for autonomous, deployable soft robots. Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.
Subfields: Biomedical Engineering, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/feaeda74182d7f9890a6ff5a1733ee21a288cbd3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Abhinav Kumar Gupta
- Carmel Majidi
- Sergey Levine
- Chelsea Finn
- Lerrel Pinto
- Pieter Abbeel
- Andy Zeng
- Jürgen Leitner
In Cognitive Neuroscience
- Abhinav Kumar Gupta
- Carmel Majidi
- Alexei A. Efros
- Andrew Zisserman
- Andrea Vedaldi
- Trevor Darrell
- Andrew Owens
- Paolo Favaro
Citations in the corpus
- Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses (2023, background): “As one of the major subjects within AI, machine learning which focuses on the development of algorithms capable to perform tasks without explicit programming, is becoming increasingly integrated with flexible sensing [5, 16, 17].”
- ReSkin: versatile, replaceable, lasting tactile skins (2021, background): “Machine learning approaches have recently been shown to effectively parse soft sensor data for robotic grasping [7, 8, 9], proprioception [10], and object classification [11], among many others [12, 13, 14].”