Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints
This repository contains codes for Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints.
Within all implementations done in a docker, we aim to support further extensions of this work and benchmarking of MRTA methods. More details are on the way!
We use Gurobi to solve Mixed Integer Linear Programming problems. In order to use this with
the docker, you will need to get a Web
License (click here) and then put the
associated gurobi.lic file in docker/gurobi.
You are responsible for your own gurobi license.
Liu, J., Neville, G., Chernova, S., & Ravichandar, H. (2024, December). Q-ITAGS: Quality-Optimized Spatio-Temporal Heterogeneous Task Allocation with a Time Budget.
In International Symposium of Robotics Research 2024.
Messing, A., & Hutchinson, S. (2020, June). Forward chaining hierarchical partial-order planning.
In International Workshop on the Algorithmic Foundations of Robotics (pp. 364-380). Springer, Cham.
Neville, G., Messing, A., Ravichandar, H., Hutchinson, S., & Chernova, S. (2021, August).
An interleaved approach to trait-based task allocation and scheduling. In 2021 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS) (pp. 1507-1514). IEEE.
Messing, A., Neville, G., Chernova, S., Hutchinson, S., & Ravichandar, H. (2021).
GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling.
The International Journal of Robotics Research.
See LICENSE