TAPAS: A Dataset for Task Assignment and Planning for Multi Agent Systems
Keywords: Dataset, Robotics, Task and Motion Planning, Multi Agent Systems
TL;DR: A Dataset for Task Assignment and Planning for Multi Agent Systems
Abstract: Obtaining real-world data for robotics tasks is harder than for other modalities such as vision and text.
The data that is currently available for robot learning is mostly set in static scenes, and deals with a single robot only.
Dealing with multiple robots comes with additional difficulties compared to single robot settings: the motion planning for multiple agents needs to take into account the movement of the other robots, and task planning needs to consider which robot a task is assigned to in addition to when a task should be done.
In this work, we present TAPAS, a simulated dataset containing task and motion plans for multiple robots acting asynchronously in the same workspace and modifying the same environment.
We consider prehensile manipulation in this dataset, and focus on various pick-and-place tasks.
We demonstrate that training using this data for predicting makespan of a task sequence enables speeding up finding low makespan sequences by ranking sequences before computing the full motion plan.
Code and datasets are open source and available on the project website, where videos of plans can be found as well.
Submission Number: 12
Loading