Recent advances of deep robotic affordance learning: a reinforcement learning perspective

X Yang, Z Ji, J Wu, YK Lai - IEEE Transactions on Cognitive …, 2023‏ - ieeexplore.ieee.org
As a popular concept proposed in the field of psychology, affordance has been regarded as
one of the important abilities that enable humans to understand and interact with the …

Network randomization: A simple technique for generalization in deep reinforcement learning

K Lee, K Lee, J Shin, H Lee - ar** skills learned from simulated environments to the real world is
favorable for many robotic applications, in which the collecting and labeling processes of …

So-nerf: Active view planning for nerf using surrogate objectives

K Lee, S Gupta, S Kim, B Makwana, C Chen… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Despite the great success of Neural Radiance Fields (NeRF), its data-gathering process
remains vague with only a general rule of thumb of sampling as densely as possible. The …

A maintenance planning framework using online and offline deep reinforcement learning

ZA Bukhsh, H Molegraaf, N Jansen - Neural Computing and Applications, 2023‏ - Springer
Cost-effective asset management is an area of interest across several industries.
Specifically, this paper develops a deep reinforcement learning (DRL) solution to …

CNN-based camera pose estimation and localization of scan images for aircraft visual inspection

X Oh, L Loh, S Foong, ZBA Koh, KL Ng… - IEEE Transactions …, 2024‏ - ieeexplore.ieee.org
General Visual Inspection is a manual inspection process regularly used to detect and
localise obvious damage on the exterior of commercial aircraft. There has been increasing …

C. dot-convolutional deep object tracker for augmented reality based purely on synthetic data

KK Thiel, F Naumann, E Jundt… - … on Visualization and …, 2021‏ - ieeexplore.ieee.org
Augmented reality applications use object tracking to estimate the pose of a camera and to
superimpose virtual content onto the observed object. Today, a number of tracking systems …

Information-aware Lyapunov-based MPC in a feedback-feedforward control strategy for autonomous robots

O Napolitano, D Fontanelli, L Pallottino… - IEEE Robotics and …, 2022‏ - ieeexplore.ieee.org
This letter proposes a feedback-feedforward control scheme that combines the benefits of an
online active sensing control strategy (the feedforward control component) to maximize the …