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About this Wiki

Following the discussions at the TAMP workshop in RSS2016, a discussion group (see Contributors below) about TAMP benchmarking was created to come up with a set of benchmark problems for TAMP. Our discussions and the resulting benchmarks can be found in the TAMP FO-DA section in the Contents menu. A paper is also available here. Our approach was to start with a set of simple benchmark problems, which can be handled by the largest number of existing systems. This requires to identify the least common denominator, in terms of requirements and problem specification, at logical and geometric levels.

However, since TAMP covers a wide range of problems, it was not possible to come up with a set of benchmarks for all TAMP problems. Therefore the Contents of the wiki are organized according to subclasses of TAMP. If you find the assumptions and requirements we have made for TAMP FO-DA incompatible with the type of problems you are interested in, please create your own set of benchmarks by adding a new entry in the Contents section.

Contents

How to use the wiki

  • This page is powered by MediaWiki, you can find instructions for editing pages here.
  • Do not modify a page for which you have not taken part in discussions.
  • For discussions, use the "Discussion" tab, or set up a discussion thread, by creating a page and adding the following code at the top: {{#useliquidthreads:1}} (see for example this page)
  • Note that there is an Atom feed for each discussion page (left menu) you can subscribe to for getting updated with new replies. You can also adjust your preferences (top right menu) for getting notified by email.

Contact

If you want to join the discussion group, contact me at fabien[dot]lagriffoul[at]oru[dot]se.

Contributors

Fabien Lagriffoul (Orebro University, Sweden.)
Neil T. Dantam (Colorado School of Mines, USA)
Caelan Garrett (Massachusetts Institute of Technology, USA)
Ali Akbari (Tech. U. of Catalonia, Spain)
Siddharth Srivastava (Arizona State University, USA)

Support

This initiative is supported by the Swedish Knowledge Foundation (KKS) project Semantic Robots. It is also supported by NSF grants IIS 1317849 and CCF-1514372 to the Kavraki Lab at Rice University, USA.

RSS Workshops

References