# Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives

arXiv: https://arxiv.org/abs/2410.00757 · HTML: https://mlsanity.com/paper/2410.00757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 4 of 4)

- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Learning Multi-Arm Manipulation Through Collaborative Teleoperation](https://mlsanity.com/paper/2012.06738/) (2020)
- [Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions](https://mlsanity.com/paper/2007.00518/) (2020)
- [A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks](https://mlsanity.com/paper/1901.07914/) (2019)

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

- [MoRI: Mixture of RL and IL Experts for Long-Horizon Manipulation Tasks](https://mlsanity.com/paper/2604.10165/) (2026)

## Abstract (arXiv)

Robotic tasks often require multiple manipulators to enhance task efficiency and speed, but this increases complexity in terms of collaboration, collision avoidance, and the expanded state-action space. To address these challenges, we propose a multi-level approach combining Reinforcement Learning (RL) and Dynamic Movement Primitives (DMP) to generate adaptive, real-time trajectories for new tasks in dynamic environments using a demonstration library. This method ensures collision-free trajectory generation and efficient collaborative motion planning. We validate the approach through experiments in the PyBullet simulation environment with UR5e robotic manipulators.