# Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments

arXiv: https://arxiv.org/abs/2303.14874 · HTML: https://mlsanity.com/paper/2303.14874/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planning](https://mlsanity.com/paper/1608.01335/) (2016)
- [A Hierarchical Architecture for Human-Robot Cooperation Processes](https://mlsanity.com/paper/2009.02807/) (2020)
- [Informed Sampling for Asymptotically Optimal Path Planning (Consolidated Version)](https://mlsanity.com/paper/1706.06454/) (2017)
- [A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation](https://mlsanity.com/paper/2106.06772/) (2021)
- [Learning Action Duration and Synergy in Task Planning for Human-Robot Collaboration](https://mlsanity.com/paper/2210.11660/) (2022)
- [Adaptive Hybrid Local-Global Sampling for Fast Informed Sampling-Based Optimal Path Planning](https://mlsanity.com/paper/2208.09318/) (2022)

## Built on by (most cited first, 8 of 8)

- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions](https://mlsanity.com/paper/2402.01108/) (2024)
- [Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration](https://mlsanity.com/paper/2511.08732/) (2025)
- [Mosaic: Runtime-Efficient Multi-Agent Embodied Planning](https://mlsanity.com/paper/2607.09603/) (2026)
- [Online Pareto-Optimal Decision-Making for Complex Tasks using Active Inference](https://mlsanity.com/paper/2406.11984/) (2024)
- [From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control](https://mlsanity.com/paper/2506.00043/) (2025)
- [Interleaving Scheduling and Motion Planning with Incremental Learning of Symbolic Space-Time Motion Abstractions](https://mlsanity.com/paper/2603.10651/) (2026)
- [Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming](https://mlsanity.com/paper/2604.19670/) (2026)

## Abstract (arXiv)

Combining symbolic and geometric reasoning in multi-agent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility that is intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.