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)
摘要
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.
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关键词
end-to-end deep learning,grasping failures,deep learning,sensing strategy,neural architectures,articulated soft hand,RBO Hand,a-posteriori detection,failure event,test-bench,failure information,deformable soft hand,distributed inertial measurement units
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