156 citations · 246 across the 43 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning of Action and Query Policies with LTL Instructions under Uncertain Event Detector
Wataru Hatanaka, Ryota Yamashina, Takamitsu Matsubara
Reinforcement learning (RL) with linear temporal logic (LTL) objectives can allow robots to carry out symbolic event plans in unknown environments. Most existing methods assume tha…
Deep Segmented DMP Networks for Learning Discontinuous Motions
Edgar Anarossi, Hirotaka Tahara, Naoto Komeno +1
Discontinuous motion which is a motion composed of multiple continuous motions with sudden change in direction or velocity in between, can be seen in state-aware robotic tasks. Suc…
Learning to Shape by Grinding: Cutting-surface-aware Model-based Reinforcement Learning
Takumi Hachimine, Jun Morimoto, Takamitsu Matsubara
Object shaping by grinding is a crucial industrial process in which a rotating grinding belt removes material. Object-shape transition models are essential to achieving automation…
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh +2
Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation.…
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
Lingwei Zhu, Zheng Chen, Matthew Schlegel +1
Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickl…