TY - GEN
T1 - Supervision via competition
T2 - 2017 IEEE International Conference on Robotics and Automation, ICRA 2017
AU - Pinto, Lerrel
AU - Davidson, James
AU - Gupta, Abhinav
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots. For supplementary video see: youtu.be/QfK3Bqhc6Sk.
AB - There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots. For supplementary video see: youtu.be/QfK3Bqhc6Sk.
UR - http://www.scopus.com/inward/record.url?scp=85028023087&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028023087&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2017.7989190
DO - 10.1109/ICRA.2017.7989190
M3 - Conference contribution
AN - SCOPUS:85028023087
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1601
EP - 1608
BT - ICRA 2017 - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 May 2017 through 3 June 2017
ER -