# An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.13511 · HTML: https://mlsanity.com/paper/2409.13511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Learning a Decentralized Multi-arm Motion Planner](https://mlsanity.com/paper/2011.02608/) (2020)
- [Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces](https://mlsanity.com/paper/1903.04959/) (2019)
- [Toward Fast and Optimal Robotic Pick-and-Place on a Moving Conveyor](https://mlsanity.com/paper/1912.08009/) (2019)

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

- [Placeit! A Framework for Learning Robot Object Placement Skills](https://mlsanity.com/paper/2510.09267/) (2025)

## Abstract (arXiv)

We introduce a novel strategy for multi-robot sorting of waste objects using Reinforcement Learning. Our focus lies on finding optimal picking strategies that facilitate an effective coordination of a multi-robot system, subject to maximizing the waste removal potential. We realize this by formulating the sorting problem as an OpenAI gym environment and training a neural network with a deep reinforcement learning algorithm. The objective function is set up to optimize the picking rate of the robotic system. In simulation, we draw a performance comparison to an intuitive combinatorial game theory-based approach. We show that the trained policies outperform the latter and achieve up to 16% higher picking rates. Finally, the respective algorithms are validated on a hardware setup consisting of a two-robot sorting station able to process incoming waste objects through pick-and-place operations.