4 papers
Towards a Pantograph-based Interventional AUV for Under-ice Measurement
Hongkyoon Byun, Jonghyuk Kim, Dikai Liu +1
This paper addresses the design of a novel interventional robotic platform, aiming to perform an autonomous sampling and measurement under the thin ice in the Antarctic environment…
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection
Tianming Wang, Wenjie Lu, Huan Yu +1
Underwater robots in shallow waters usually suffer from strong wave forces, which may frequently exceed robot's control constraints. Learning-based controllers are suitable for dis…
A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances
Wenjie Lu, Dikai Liu
Reinforcement Learning (RL) is limited in practice by its gray-box nature, which is responsible for insufficient trustiness from users, unsatisfied interpretation for human interve…
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
Tianming Wang, Wenjie Lu, Zheng Yan +1
This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OBserver Network (DOB-Net), for robots operating in environments where disturban…