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Review of reinforcement learning for robotic gras**: Analysis and recommendations
This review paper provides a comprehensive analysis of over 100 research papers focused
on the challenges of robotic gras** and the effectiveness of various machine learning …
on the challenges of robotic gras** and the effectiveness of various machine learning …
Deep-reinforcement-learning-based path planning for industrial robots using distance sensors as observation
Traditionally, collision-free path planning for industrial robots is realized by sampling-based
algorithms such as RRT (Rapidly-exploring Random Tree), PRM (Probabilistic Roadmap) …
algorithms such as RRT (Rapidly-exploring Random Tree), PRM (Probabilistic Roadmap) …
Details make a difference: Object state-sensitive neurorobotic task planning
The state of an object reflects its current status or condition and is important for a robot's task
planning and manipulation. However, detecting an object's state and generating a state …
planning and manipulation. However, detecting an object's state and generating a state …
Model mediated teleoperation with a hand-arm exoskeleton in long time delays using reinforcement learning
Telerobotic systems must adapt to new environmental conditions and deal with high
uncertainty caused by long-time delays. As one of the best alternatives to human-level …
uncertainty caused by long-time delays. As one of the best alternatives to human-level …
Multi-vehicle mixed-reality reinforcement learning for autonomous multi-lane driving
R Mitchell, J Fletcher, J Panerati, A Prorok - ar** and Manipulation
Z Deng - 2019 - ediss.sub.uni-hamburg.de
Dexterous gras** and manipulation of objects are fundamental abilities for robots. Our aim
is to endow robots with human-like gras** and manipulation capabilities. Four issues are …
is to endow robots with human-like gras** and manipulation capabilities. Four issues are …
Task-Based Feature Learning and Enhancement for Bandwidth-Limited Applications
JA White - 2022 - figshare.swinburne.edu.au
This research introduces an entirely new framework for vision processing that learns task-
based visual features and enhances them in images to guide human action in vision-based …
based visual features and enhances them in images to guide human action in vision-based …
Adaptive Model Mediated Control Using Reinforcement Learning
H Beik-Mohammadi - 2020 - elib.dlr.de
Due to similarities in learning techniques, Reinforcement Learning (RL) is the closest
alternative to human-level intelligence. Teleoperation systems using RL can adapt to new …
alternative to human-level intelligence. Teleoperation systems using RL can adapt to new …