# Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator

arXiv: https://arxiv.org/abs/2412.03012 · HTML: https://mlsanity.com/paper/2412.03012/

## Key facts

- Cited by 10 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 28 of its 31 references that are in the map.
- First posted on arXiv 4 Dec 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, 28 of 28)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Penalized Proximal Policy Optimization for Safe Reinforcement Learning](https://mlsanity.com/paper/2205.11814/) (2022)
- [Visual Whole-Body Control for Legged Loco-Manipulation](https://mlsanity.com/paper/2403.16967/) (2024)
- [DTC: Deep Tracking Control](https://mlsanity.com/paper/2309.15462/) (2023)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation](https://mlsanity.com/paper/2304.00410/) (2023)
- [Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion](https://mlsanity.com/paper/2303.11330/) (2023)
- [Perceptive Locomotion through Nonlinear Model Predictive Control](https://mlsanity.com/paper/2208.08373/) (2022)
- [Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators](https://mlsanity.com/paper/2201.03871/) (2022)
- [Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Leg](https://mlsanity.com/paper/2402.10837/) (2024)
- [Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators](https://mlsanity.com/paper/2303.05486/) (2023)
- [Learning Force Control for Legged Manipulation](https://mlsanity.com/paper/2405.01402/) (2024)
- [RoLoMa: Robust Loco-Manipulation for Quadruped Robots with Arms](https://mlsanity.com/paper/2203.01446/) (2022)
- [Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation](https://mlsanity.com/paper/2211.10270/) (2022)
- [Benchmarking Potential Based Rewards for Learning Humanoid Locomotion](https://mlsanity.com/paper/2307.10142/) (2023)
- [Evaluation of Constrained Reinforcement Learning Algorithms for Legged Locomotion](https://mlsanity.com/paper/2309.15430/) (2023)
- [Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots](https://mlsanity.com/paper/2010.06322/) (2020)
- [Rolling in the Deep -- Hybrid Locomotion for Wheeled-Legged Robots using Online Trajectory Optimization](https://mlsanity.com/paper/1909.07193/) (2019)
- [Arm-Constrained Curriculum Learning for Loco-Manipulation of the Wheel-Legged Robot](https://mlsanity.com/paper/2403.16535/) (2024)
- [Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots](https://mlsanity.com/paper/2104.04247/) (2021)

## Built on by (most cited first, 10 of 10)

- [Multi-Embodiment Robotic Retargeting via Guided Diffusion Model](https://mlsanity.com/paper/2505.20857/) (2025)
- [LIPM-Guided Reinforcement Learning for Stable and Perceptive Locomotion in Bipedal Robots](https://mlsanity.com/paper/2509.09106/) (2025)
- [MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion](https://mlsanity.com/paper/2508.10423/) (2025)
- [CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection](https://mlsanity.com/paper/2604.07457/) (2026)
- [SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning](https://mlsanity.com/paper/2604.24729/) (2026)
- [A Practical Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/2505.08295/) (2025)
- [Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation](https://mlsanity.com/paper/2606.07506/) (2026)
- [WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots](https://mlsanity.com/paper/2607.24036/) (2026)
- [Spatiotemporal Agility: Time-Constrained Reinforcement Learning for Vision-Guided Dynamic Quadrupedal Interception](https://mlsanity.com/paper/2608.06907/) (2026)
- [Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation](https://mlsanity.com/paper/2609.13779/) (2026)

## Abstract (arXiv)

In this paper, we study the whole-body loco-manipulation problem using reinforcement learning (RL). Specifically, we focus on the problem of how to coordinate the floating base and the robotic arm of a wheeled-quadrupedal manipulator robot to achieve direct six-dimensional (6D) end-effector (EE) pose tracking in task space. Different from conventional whole-body loco-manipulation problems that track both floating-base and end-effector commands, the direct EE pose tracking problem requires inherent balance among redundant degrees of freedom in the whole-body motion. We leverage RL to solve this challenging problem. To address the associated difficulties, we develop a novel reward fusion module (RFM) that systematically integrates reward terms corresponding to different tasks in a nonlinear manner. In such a way, the inherent multi-stage and hierarchical feature of the loco-manipulation problem can be carefully accommodated. By combining the proposed RFM with the a teacher-student RL training paradigm, we present a complete RL scheme to achieve 6D EE pose tracking for the wheeled-quadruped manipulator robot. Extensive simulation and hardware experiments demonstrate the significance of the RFM. In particular, we enable smooth and precise tracking performance, achieving state-of-the-art tracking position error of less than 5 cm, and rotation error of less than 0.1 rad. Please refer to https://clearlab-sustech.github.io/RFM_loco_mani/ for more experimental videos.