Abstract
We present a deep convolutional neural network which is capable to distinguish between different contact states in robotic manipulation tasks. By integrating spatial and temporal tactile sensor data from a piezo-resistive sensor array through deep learning techniques, the network is not only able to classify the contact state into stable versus slipping, but also to distinguish between rotational and translation slippage. We evaluated different network layouts and reached a final classification rate of more than 97 %. Using consumer class GPUs, slippage and rotation events can be detected within 10 ms, which is still feasible for adaptive grasp control.
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Notes
- 1.
Called Myrmex hereafter.
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Acknowledgments
The research leading to these results has received funding from the European Community’s Framework Programme Horizon 2020 – under grant agreement No 644938 – SARAFun and was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).
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Meier, M., Patzelt, F., Haschke, R., Ritter, H.J. (2016). Tactile Convolutional Networks for Online Slip and Rotation Detection. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_2
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