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Improved considerations for planning with dynamics remains
an area of future work for TMKit.
An ongoing need in TMP is support to compare and
benchmark different TMP algorithms and implementations. We believe that TMKit, as an extensible framework
supporting common formats such as PDDL and URDF, can
help meet that need. Furthermore, modular components,
such as the scene graph compiler (see Figure 6), could aid
the development of alternative TMP methods and implementations. We hope that TMKit will be a useful tool for
other researchers to evaluate existing algorithms and extend
to new approaches.
Acknowledgments
We thank Andrew Wells, Cannon Lewis, and Logan Farrell
for their help with the demonstration of TMKit on the
UR5. This work was supported in part by grants NSF IIS1317849 and NSF CCF-1514372, by the ERIT program of
the K2I Institute at Rice University, and by the Rice University Funds.
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Neil T. Dantam, Department of Computer Science, Rice University, Houston, United States, and Department of Computer
Science, Colorado School of Mines, Golden, United States.
E-mail: ndantam@mines.edu.
Swarat Chaudhuri, Department of Computer Science, Rice
University, Houston, United States. E-mail: swarat@rice.edu.
Lydia E. Kavraki, Department of Computer Science, Rice University, Houston, United States. E-mail: kavraki@rice.edu.
http://tmkit.kavrakilab.org http://www.openrave.org/ http://www.orocos.org/kdl http://moveit.ros.org
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