# Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.02637 · HTML: https://mlsanity.com/paper/2003.02637/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation](https://mlsanity.com/paper/2107.13545/) (2021)
- [Adaptive Mobile Manipulation for Articulated Objects In the Open World](https://mlsanity.com/paper/2401.14403/) (2024)
- [Robot Learning of Mobile Manipulation with Reachability Behavior Priors](https://mlsanity.com/paper/2203.04051/) (2022)
- [Causal Policy Gradient for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2305.04866/) (2023)
- [N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments](https://mlsanity.com/paper/2206.08737/) (2022)
- [Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations](https://mlsanity.com/paper/2104.13907/) (2021)
- [Versatile Loco-Manipulation through Flexible Interlimb Coordination](https://mlsanity.com/paper/2506.07876/) (2025)
- [A Reactive Framework for Whole-Body Motion Planning of Mobile Manipulators Combining Reinforcement Learning and SDF-Constrained Quadratic Programmi](https://mlsanity.com/paper/2503.23975/) (2025)
- [Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviors](https://mlsanity.com/paper/2410.21059/) (2024)

## Abstract (arXiv)

Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times. In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.