# Arm-Constrained Curriculum Learning for Loco-Manipulation of the Wheel-Legged Robot

arXiv: https://arxiv.org/abs/2403.16535 · HTML: https://mlsanity.com/paper/2403.16535/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2024.

## Built on by (most cited first, 8 of 8)

- [Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization](https://mlsanity.com/paper/2505.15660/) (2025)
- [GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation](https://mlsanity.com/paper/2505.11865/) (2025)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator](https://mlsanity.com/paper/2412.03012/) (2024)
- [Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum](https://mlsanity.com/paper/2511.11218/) (2025)
- [End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy](https://mlsanity.com/paper/2508.07611/) (2025)
- [Effective Reinforcement Learning Control using Conservative Soft Actor-Critic](https://mlsanity.com/paper/2505.03356/) (2025)
- [ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking](https://mlsanity.com/paper/2602.06445/) (2026)

## Abstract (arXiv)

Incorporating a robotic manipulator into a wheel-legged robot enhances its agility and expands its potential for practical applications. However, the presence of potential instability and uncertainties presents additional challenges for control objectives. In this paper, we introduce an arm-constrained curriculum learning architecture to tackle the issues introduced by adding the manipulator. Firstly, we develop an arm-constrained reinforcement learning algorithm to ensure safety and stability in control performance. Additionally, to address discrepancies in reward settings between the arm and the base, we propose a reward-aware curriculum learning method. The policy is first trained in Isaac gym and transferred to the physical robot to do dynamic grasping tasks, including the door-opening task, fan-twitching task and the relay-baton-picking and following task. The results demonstrate that our proposed approach effectively controls the arm-equipped wheel-legged robot to master dynamic grasping skills, allowing it to chase and catch a moving object while in motion. Please refer to our website (https://acodedog.github.io/wheel-legged-loco-manipulation) for the code and supplemental videos.