# A Reactive Framework for Whole-Body Motion Planning of Mobile Manipulators Combining Reinforcement Learning and SDF-Constrained Quadratic Programmi

arXiv: https://arxiv.org/abs/2503.23975 · HTML: https://mlsanity.com/paper/2503.23975/

## 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 17 of its 24 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## 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, 17 of 17)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Randomized Ensembled Double Q-Learning: Learning Fast Without a Model](https://mlsanity.com/paper/2101.05982/) (2021)
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics](https://mlsanity.com/paper/2005.04269/) (2020)
- [Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors](https://mlsanity.com/paper/2001.02811/) (2020)
- [Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight](https://mlsanity.com/paper/1907.01531/) (2019)
- [Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians](https://mlsanity.com/paper/2102.13073/) (2021)
- [Robot Learning of Mobile Manipulation with Reachability Behavior Priors](https://mlsanity.com/paper/2203.04051/) (2022)
- [A Holistic Approach to Reactive Mobile Manipulation](https://mlsanity.com/paper/2109.04749/) (2021)
- [A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation](https://mlsanity.com/paper/2202.12385/) (2022)
- [N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments](https://mlsanity.com/paper/2206.08737/) (2022)
- [Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation](https://mlsanity.com/paper/2211.10270/) (2022)
- [Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning](https://mlsanity.com/paper/2003.02637/) (2020)
- [Mobile Manipulator for Autonomous Localization, Grasping and Precise Placement of Construction Material in a Semi-structured Environment](https://mlsanity.com/paper/2011.07972/) (2020)
- [NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators](https://mlsanity.com/paper/2010.08686/) (2020)
- [A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control](https://mlsanity.com/paper/2203.03391/) (2022)

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

- [Safety-aware Skill Adaptation for Reinforcement Learning in Dynamic Environments](https://mlsanity.com/paper/2609.11433/) (2026)

## Abstract (arXiv)

As an important branch of embodied artificial intelligence, mobile manipulators are increasingly applied in intelligent services, but their redundant degrees of freedom also limit efficient motion planning in cluttered environments. To address this issue, this paper proposes a hybrid learning and optimization framework for reactive whole-body motion planning of mobile manipulators. We develop the Bayesian distributional soft actor-critic (Bayes-DSAC) algorithm to improve the quality of value estimation and the convergence performance of the learning. Additionally, we introduce a quadratic programming method constrained by the signed distance field to enhance the safety of the obstacle avoidance motion. We conduct experiments and make comparison with standard benchmark. The experimental results verify that our proposed framework significantly improves the efficiency of reactive whole-body motion planning, reduces the planning time, and improves the success rate of motion planning. Additionally, the proposed reinforcement learning method ensures a rapid learning process in the whole-body planning task. The novel framework allows mobile manipulators to adapt to complex environments more safely and efficiently.