TY - GEN
T1 - Variability and predictability in tactile sensing during grasping
AU - Wan, Qian
AU - Adams, Ryan P.
AU - Howe, Robert D.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/8
Y1 - 2016/6/8
N2 - Robotic manipulation in unstructured environments requires grasping a wide range of objects. Tactile sensing is presumed to provide essential information in this context, but there has been little work examining the tactile sensor signals produced during realistic manipulation tasks. This paper presents tactile sensor data from grasping a generic object in thousands of trials. Position error between the hand and object was varied to model the uncertainty in real-world grasping, and a grasp outcome prediction was done using only tactile sensors. Results show that tactile signals are highly variable despite good repeatability in grasping conditions. The observed variability appears to be intrinsic to the grasping process, due to the mechanical coupling between fingers as they contact the object in parallel, as well as numerous factors such as frictional effects and inaccuracies in the robot hand. Using a simple machine learning algorithm, grasp outcome prediction based purely on tactile sensors is not reliable enough for real-world responsibilities. These results have implications for improved tactile sensor system and controller design, as well as signal processing and machine learning methods.
AB - Robotic manipulation in unstructured environments requires grasping a wide range of objects. Tactile sensing is presumed to provide essential information in this context, but there has been little work examining the tactile sensor signals produced during realistic manipulation tasks. This paper presents tactile sensor data from grasping a generic object in thousands of trials. Position error between the hand and object was varied to model the uncertainty in real-world grasping, and a grasp outcome prediction was done using only tactile sensors. Results show that tactile signals are highly variable despite good repeatability in grasping conditions. The observed variability appears to be intrinsic to the grasping process, due to the mechanical coupling between fingers as they contact the object in parallel, as well as numerous factors such as frictional effects and inaccuracies in the robot hand. Using a simple machine learning algorithm, grasp outcome prediction based purely on tactile sensors is not reliable enough for real-world responsibilities. These results have implications for improved tactile sensor system and controller design, as well as signal processing and machine learning methods.
UR - http://www.scopus.com/inward/record.url?scp=84977520376&partnerID=8YFLogxK
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U2 - 10.1109/ICRA.2016.7487129
DO - 10.1109/ICRA.2016.7487129
M3 - Conference contribution
AN - SCOPUS:84977520376
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 158
EP - 164
BT - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Y2 - 16 May 2016 through 21 May 2016
ER -