Toward synergic learning for autonomous manipulation of deformable tissues via surgical robots: An approximate q-learning approach
In this paper, we present a synergic learning algorithm to address the task of indirect
manipulation of an unknown deformable tissue. Tissue manipulation is a common yet
challenging task in various surgical interventions, which makes it a good candidate for
robotic automation. We propose using a linear approximate Q-learning method in which
human knowledge contributes to selecting useful yet simple features of tissue manipulation
while the algorithm learns to take optimal actions and accomplish the task. The algorithm is …
manipulation of an unknown deformable tissue. Tissue manipulation is a common yet
challenging task in various surgical interventions, which makes it a good candidate for
robotic automation. We propose using a linear approximate Q-learning method in which
human knowledge contributes to selecting useful yet simple features of tissue manipulation
while the algorithm learns to take optimal actions and accomplish the task. The algorithm is …
Toward synergic learning for autonomous manipulation of deformable tissues via surgical robots: an approximate Q-learning approach
S Aghajani Pedram, P Walker Ferguson… - arXiv e …, 2019 - ui.adsabs.harvard.edu
In this paper, we present a synergic learning algorithm to address the task of indirect
manipulation of an unknown deformable tissue. Tissue manipulation is a common yet
challenging task in various surgical interventions, which makes it a good candidate for
robotic automation. We propose using a linear approximate Q-learning method in which
human knowledge contributes to selecting useful yet simple features of tissue manipulation
while the algorithm learns to take optimal actions and accomplish the task. The algorithm is …
manipulation of an unknown deformable tissue. Tissue manipulation is a common yet
challenging task in various surgical interventions, which makes it a good candidate for
robotic automation. We propose using a linear approximate Q-learning method in which
human knowledge contributes to selecting useful yet simple features of tissue manipulation
while the algorithm learns to take optimal actions and accomplish the task. The algorithm is …
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