# A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks

arXiv: https://arxiv.org/abs/2301.05346 · HTML: https://mlsanity.com/paper/2301.05346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 20 of 20)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Distral: Robust Multitask Reinforcement Learning](https://mlsanity.com/paper/1707.04175/) (2017)
- [MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies](https://mlsanity.com/paper/1905.09808/) (2019)
- [Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention](https://mlsanity.com/paper/2104.11203/) (2021)
- [Composable Deep Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/1803.06773/) (2018)
- [Divide-and-Conquer Reinforcement Learning](https://mlsanity.com/paper/1711.09874/) (2017)
- [Riemannian Motion Policies](https://mlsanity.com/paper/1801.02854/) (2018)
- [Composing Task-Agnostic Policies with Deep Reinforcement Learning](https://mlsanity.com/paper/1905.10681/) (2019)
- [Learning to Compose Skills](https://mlsanity.com/paper/1711.11289/) (2017)
- [An Optimal Task Allocation Strategy for Heterogeneous Multi-Robot Systems](https://mlsanity.com/paper/1903.08641/) (2019)
- [Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping](https://mlsanity.com/paper/1910.02646/) (2019)
- [Composable Geometric Motion Policies using Multi-Task Pullback Bundle Dynamical Systems](https://mlsanity.com/paper/2101.01297/) (2021)
- [A Set-Theoretic Approach to Multi-Task Execution and Prioritization](https://mlsanity.com/paper/2003.02968/) (2020)

## Built on by (most cited first, 1 of 1)

- [Value Iteration for Learning Concurrently Executable Robotic Control Tasks](https://mlsanity.com/paper/2504.01174/) (2025)

## Abstract (arXiv)

This paper presents a constrained-optimization formulation for the prioritized execution of learned robot tasks. The framework lends itself to the execution of tasks encoded by value functions, such as tasks learned using the reinforcement learning paradigm. The tasks are encoded as constraints of a convex optimization program by using control Lyapunov functions. Moreover, an additional constraint is enforced in order to specify relative priorities between the tasks. The proposed approach is showcased in simulation using a team of mobile robots executing coordinated multi-robot tasks.