# Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces

arXiv: https://arxiv.org/abs/2110.04089 · HTML: https://mlsanity.com/paper/2110.04089/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [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)
- [An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem](https://mlsanity.com/paper/1907.10360/) (2019)
- [Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space](https://mlsanity.com/paper/2010.00780/) (2020)
- [Manipulation of Articulated Objects using Dual-arm Robots via Answer Set Programming](https://mlsanity.com/paper/2010.01164/) (2020)
- [A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios](https://mlsanity.com/paper/2009.06423/) (2020)
- [A Task-Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks](https://mlsanity.com/paper/2104.01549/) (2021)

## Built on by (most cited first, 7 of 7)

- [Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2302.07114/) (2023)
- [A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks](https://mlsanity.com/paper/2503.07700/) (2025)
- [Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlaps](https://mlsanity.com/paper/2511.12203/) (2025)
- [MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments](https://mlsanity.com/paper/2306.06543/) (2023)
- [A Framework for Task and Motion Planning based on Expanding AND/OR Graphs](https://mlsanity.com/paper/2509.00317/) (2025)
- [Learning Motion Feasibility from Point Clouds in Cluttered Environments](https://mlsanity.com/paper/2606.26700/) (2026)
- [A Horizon-slicing Approach to Minimum Obstacle Displacement Planning for Robot Navigation](https://mlsanity.com/paper/2609.22974/) (2026)

## Abstract (arXiv)

We present an AND/OR graph-based, integrated multi-robot task and motion planning approach which (i) performs task allocation coordinating the activity of a given number of robots, and (ii) is capable of handling tasks which involve an a priori unknown number of object re-arrangements, such as those involved in retrieving objects from cluttered workspaces. Such situations may arise, for example, in search and rescue scenarios, while locating/picking a cluttered object of interest. The corresponding problem falls under the category of planning in clutter. One of the challenges while planning in clutter is that the number of object re-arrangements required to pick the target object is not known beforehand, in general. Moreover, such tasks can be decomposed in a variety of ways, since different cluttering object re-arrangements are possible to reach the target object. In our approach, task allocation and decomposition is achieved by maximizing a combined utility function. The allocated tasks are performed by an integrated task and motion planner, which is robust to the requirement of an unknown number of re-arrangement tasks. We demonstrate our results with experiments in simulation on two Franka Emika manipulators.