TY - GEN
T1 - Extrinsic dexterity through active slip control using deep predictive models
AU - Stepputtis, Simon
AU - Yang, Yezhou
AU - Ben Amor, Hani
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - We present a machine learning methodology for actively controlling slip, in order to increase robot dexterity. Leveraging recent insights in deep learning, we propose a Deep Predictive Model that uses tactile sensor information to reason about slip and its future influence on the manipulated object. The obtained information is then used to precisely manipulate objects within a robot end-effector using external perturbations imposed by gravity or acceleration. We show in a set of experiments that this approach can be used to increase a robot's repertoire of motor skills.
AB - We present a machine learning methodology for actively controlling slip, in order to increase robot dexterity. Leveraging recent insights in deep learning, we propose a Deep Predictive Model that uses tactile sensor information to reason about slip and its future influence on the manipulated object. The obtained information is then used to precisely manipulate objects within a robot end-effector using external perturbations imposed by gravity or acceleration. We show in a set of experiments that this approach can be used to increase a robot's repertoire of motor skills.
UR - http://www.scopus.com/inward/record.url?scp=85063146994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063146994&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2018.8461055
DO - 10.1109/ICRA.2018.8461055
M3 - Conference contribution
AN - SCOPUS:85063146994
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3180
EP - 3185
BT - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Y2 - 21 May 2018 through 25 May 2018
ER -