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Efficient Algorithms for Joint Task and Motion Planning in Robotics

EasyChair Preprint no. 12326

6 pagesDate: February 29, 2024


This abstract explores recent advancements in the development of efficient algorithms for joint task and motion planning (JTMP). The objective of JTMP is to generate feasible plans that seamlessly integrate high-level task specifications with low-level motion constraints, enabling robots to accomplish tasks effectively and adaptively. Traditional approaches to task and motion planning often suffer from computational complexity, limiting their scalability and real-time applicability. However, recent research has focused on addressing these challenges by leveraging techniques such as search-space pruning, heuristic guidance, and parallel computation. By exploiting the structure of the problem and incorporating domain-specific knowledge, these algorithms can efficiently explore the vast solution space and find optimal or near-optimal plans.

Keyphrases: motion, planning, Robotics

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Julia Anderson and Jane Smith},
  title = {Efficient Algorithms for Joint Task and Motion Planning in Robotics},
  howpublished = {EasyChair Preprint no. 12326},

  year = {EasyChair, 2024}}
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