# CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2403.18459 · HTML: https://mlsanity.com/paper/2403.18459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [A Survey of Behavior Trees in Robotics and AI](https://mlsanity.com/paper/2005.05842/) (2020)
- [Taming the Panda with Python: A Powerful Duo for Seamless Robotics Programming and Integration](https://mlsanity.com/paper/2307.07633/) (2023)
- [Argumentation for Explainable Scheduling (Full Paper with Proofs)](https://mlsanity.com/paper/1811.05437/) (2018)
- [Optimized Execution of PDDL Plans using Behavior Trees](https://mlsanity.com/paper/2101.01964/) (2021)
- [Drake: An Efficient Executive for Temporal Plans with Choice](https://mlsanity.com/paper/1401.4606/) (2014)

## Abstract (arXiv)

Assembly processes involving humans and robots are challenging scenarios because the individual activities and access to shared workspace have to be coordinated. Fixed robot programs leave no room to diverge from a fixed protocol. Working on such a process can be stressful for the user and lead to ineffective behavior or failure. We propose a novel approach of online constraint-based scheduling in a reactive execution control framework facilitating behavior trees called CoBOS. This allows the robot to adapt to uncertain events such as delayed activity completions and activity selection (by the human). The user will experience less stress as the robotic coworkers adapt their behavior to best complement the human-selected activities to complete the common task. In addition to the improved working conditions, our algorithm leads to increased efficiency, even in highly uncertain scenarios. We evaluate our algorithm using a probabilistic simulation study with 56000 experiments. We outperform all other compared methods by a margin of 4-10%. Initial real robot experiments using a Franka Emika Panda robot and human tracking based on HTC Vive VR gloves look promising.