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Found 5 papers, 1 papers with code
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OBserver Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities.
A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances
This paper proposes an Attention-based Abstraction (A${}^2$) approach to extract a finite-state automaton, referred to as a Key Moore Machine Network (KMMN), to capture the switching mechanisms exhibited by the DOB-net in dealing with multiple such POMDPs.
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection
In this paper, we propose a transfer learning framework that adapts a control policy for excessive disturbance rejection of an underwater robot under dynamics model mismatch.
Neuroadaptation in Physical Human-Robot Collaboration
Robots for physical Human-Robot Collaboration (pHRC) systems need to change their behavior and how they operate in consideration of several factors, such as the performance and intention of a human co-worker and the capabilities of different human-co-workers in collision avoidance and singularity of the robot operation.
Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey
Furthermore, in relation to the creation and application of foundation models for weather and climate data understanding, we delve into the field's prevailing challenges, offer crucial insights, and propose detailed avenues for future research.
