Task and Motion Informed Trees (TMIT*)

Source Code

A public implementation of TMIT* is available in its GitHub repository.

Description

Task and Motion Informed Trees (TMIT*) is a practical approach to almost-surely asymptotically optimal integrated task and motion planning (TMP). It uses relaxed symbolic planning and anytime satisfaction of action preconditions to efficiently discover candidate symbolic plans. It then uses AIT* to lazily validate these symbolic plans and find and improve compatible motion plans. This hierarchical, interleaved approach allows TMIT* to share information between all aspects of the TMP search and delay computationally expensive operations until necessary. As a result, TMIT* finds initial solutions quickly and converges toward the optimal integrated task and motion plan in an anytime manner.

Bibliography

Authors
1. W. Thomason
2. M. P. Strub
3. J. D. Gammell
Title
Task and Motion Informed Trees (TMIT*): Almost-surely asymptotically optimal integrated task and motion planning
Publication
Journal
IEEE Robotics and Automation Letters (RA-L)
Volume
7
Number
4
Pages
11370–11377
Date
Oct. 2022
Notes
Presented at IROS 2022
Code

Presentations

PDFs

Digital Object Identifier (DOI)

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