Authors:
Kai Zhang
1
;
2
;
Eric Lucet
2
;
Julien Alexandre Dit Sandretto
1
;
Selma Kchir
2
and
David Filliat
3
;
1
Affiliations:
1
U2IS, ENSTA Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France
;
2
Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France
;
3
FLOWERS, INRIA, ENSTA Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France
Keyword(s):
Task and Motion Planning, Simulation Environment, Learning Methods.
Abstract:
Robots are required to perform more and more complicated tasks, which raises the requirement of more intelligent planning algorithms. As a domain having been explored for decades, task and motion planning (TAMP) methods have achieved significant results, but several challenges remain to be solved. This paper summarizes the development of TAMP from solving objectives, simulation environments, methods and remaining limitations. In particular, it compares different simulation environments and methods used in different tasks aiming to provide a practical guide and overview for the beginners.