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Recent advances of deep robotic affordance learning: a reinforcement learning perspective
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 …
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
So-nerf: Active view planning for nerf using surrogate objectives
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 …
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
Cost-effective asset management is an area of interest across several industries.
Specifically, this paper develops a deep reinforcement learning (DRL) solution to …
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
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 …
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 …
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
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 …
online active sensing control strategy (the feedforward control component) to maximize the …