To grasp or not to grasp: an end-to-end deep-learning approach for predicting grasping failures in soft hands
Name
ROSO20_0057_FI.pdf
Description
Accepted version
Size
1.89 MB
Format
Unknown
Checksum (MD5)
ed4650eb1c2898e3e433e9c12daf10ff
Author(s)
Arapi, Visar
Zhang, Yujie
Averta, Giuseppe
Catalano, Manuel G.
Rus, Daniela L
Santina, Cosimo Della
Bianchi, Matteo
Date Issued
May 2020
Journal
2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
2020. "To grasp or not to grasp: an end-to-end deep-learning approach for predicting grasping failures in soft hands." 2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020.
Version
Author's final manuscript
Abstract
© 2020 IEEE. This paper tackles the challenge of predicting grasp failures in soft hands before they happen, by combining deep learning with a sensing strategy based on distributed Inertial Measurement Units. We propose two neural architectures, which we implemented and tested with an articulated soft hand-the Pisa/IIT SoftHand-and a continuously deformable soft hand-the RBO Hand. The first architecture (Classifier) implements a-posteriori detection of the failure event, serving as a test-bench to assess the possibility of extracting failure information from the discussed input signals. This network reaches up to 100% of accuracy within our experimental validation. Motivated by these results, we introduce a second architecture (Predictor), which is the main contribution of the paper. This network works on-line and takes as input a multidimensional continuum stream of raw signals coming from the Inertial Measurement Units. The network is trained to predict the occurrence in the near future of a failure event. The Predictor detects 100% of failures with both hands, with the detection happening on average 1.96 seconds before the actual failing occurs-leaving plenty of time to an hypothetical controller to react.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ROBOSOFT48309.2020.9116041
