Task-Aware Waypoint Sampling for Robotic Planning

Sarah Keren, Gerard Canal, Michael Cashmore

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

15 Downloads (Pure)


To achieve a complex task, a robot often needs to navigate in a physical space in order to complete activities in different locations. For example, it may need to inspect several structures, making multiple observations of each structure from different perspectives. Typically, the positions from which these activities can be performed are represented as waypoints – discrete positions that are sampled from the continuous physical space and used to find a task plan. Existing approaches to waypoint selection either iteratively consider the entire space or use domain knowledge to consider each activity separately. This can lead to task planning problems that are more complex than is necessary or to plans of compromised quality. Moreover, all previous approaches only consider geometric constraints that can be imposed on the waypoint selection process. We present Task-Aware Waypoint Sampling (TAWS), which offers two key novelties. First, it is an anytime approach that combines the benefits of random sampling with the use of domain knowledge in waypoint selection by performing a one-time computation of the connectivity graph from which waypoints are sampled. In addition, TAWS is the first approach that accounts for performance preferences, which are preferences a system operator may have about the generated task plan. These can account, for example, for areas near doorways where it is preferable that the robot does not stop to perform activities. We demonstrate the performance benefits of our approach on simulated automated manufacturing tasks.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Automated Planning and Scheduling
Number of pages9
Publication statusPublished - 17 May 2021


  • Integrated Task And Motion Planning
  • Planning With Uncertainty In Robotics
  • Real-world Robotic Planning Applications


Dive into the research topics of 'Task-Aware Waypoint Sampling for Robotic Planning'. Together they form a unique fingerprint.

Cite this