TY - GEN
T1 - Estimating perturbations from experience using neural networks and information transfer
AU - Berger, Erik
AU - Vogt, David
AU - Grehl, Steve
AU - Jung, Bernhard
AU - Ben Amor, Hani
PY - 2016/11/28
Y1 - 2016/11/28
N2 - In order to ensure safe operation, robots must be able to reliably detect behavior perturbations that result from unexpected physical interactions with their environment and human co-workers. While some robots provide firmware force sensors that generate rough force estimates, more accurate force measurements are usually achieved with dedicated force-torque sensors. However, such sensors are often heavy, expensive and require an additional power supply. In the case of lightweight manipulators, the already limited payload capabilities may be reduced in a significant way. This paper presents an experience-based approach for accurately estimating external forces being applied to a robot without the need for a forcetorque sensor. Using Information Transfer, a subset of sensors relevant to the executed behavior are identified from a larger set of internal sensors. Models mapping robot sensor data to force-torque measurements are learned using a neural network. These models can be used to predict the magnitude and direction of perturbations from affordable, proprioceptive sensors only. Experiments with a UR5 robot show that our method yields force estimates with accuracy comparable to a dedicated force-torque sensor. Moreover, our method yields a substantial improvement in accuracy over force-torque values provided by the robot firmware.
AB - In order to ensure safe operation, robots must be able to reliably detect behavior perturbations that result from unexpected physical interactions with their environment and human co-workers. While some robots provide firmware force sensors that generate rough force estimates, more accurate force measurements are usually achieved with dedicated force-torque sensors. However, such sensors are often heavy, expensive and require an additional power supply. In the case of lightweight manipulators, the already limited payload capabilities may be reduced in a significant way. This paper presents an experience-based approach for accurately estimating external forces being applied to a robot without the need for a forcetorque sensor. Using Information Transfer, a subset of sensors relevant to the executed behavior are identified from a larger set of internal sensors. Models mapping robot sensor data to force-torque measurements are learned using a neural network. These models can be used to predict the magnitude and direction of perturbations from affordable, proprioceptive sensors only. Experiments with a UR5 robot show that our method yields force estimates with accuracy comparable to a dedicated force-torque sensor. Moreover, our method yields a substantial improvement in accuracy over force-torque values provided by the robot firmware.
UR - http://www.scopus.com/inward/record.url?scp=85006401324&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006401324&partnerID=8YFLogxK
U2 - 10.1109/IROS.2016.7759052
DO - 10.1109/IROS.2016.7759052
M3 - Conference contribution
AN - SCOPUS:85006401324
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 176
EP - 181
BT - IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016
Y2 - 9 October 2016 through 14 October 2016
ER -