Wenzhen Yuan1, Zhu Chenzhuo2, Andrew Owens1, Mandayam Srinivasan1, Edward Adelson1
11:40 - 11:45 | Tue 30 May | Room 4711/4712 | TUB7.3
Hardness is among the most important attributes of an object that humans learn about through touch. However, approaches for robots to estimate hardness are limited, due to the lack of information provided by current tactile sensors. In this work, we address these limitations by introducing a novel method for hardness estimation, based on the GelSight tactile sensor, and the method does not require accurate control of contact conditions or the shape of objects. A GelSight has a soft contact interface, and provides high resolution tactile images of contact geometry, as well as contact force and slip conditions. In this paper, we try to use the sensor to measure hardness of objects with multiple shapes, under a loosely controlled contact condition. The contact is made manually or by a robot hand, while the force and trajectory are unknown and uneven. We analyze the data using a deep convolutional (and recurrent) neural network. Experimental results show that for the objects ranging from 8 to 87 in Shore 00 scale hardness, the neural net model can measure the hardness of standard silicone samples with an RMSE of 5.2, and estimate different hardness level of natural objects.