# Reinforcement Learning-Based Multi-Terrain Whole-Body Control for a Quadruped Mobile Manipulator

- Source: arXiv cs.RO
- Published: 2026-10-19T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/y4hwmwjpyu9bhlen3687wn859
- Original: https://arxiv.org/abs/2610.23548

## Summary

The paper proposes a reinforcement learning-based whole-body control framework for quadruped loco-manipulation on complex terrain. The framework combines a hybrid task representation with task-prioritized whole-body learning to coordinate locomotion and manipulation while preserving task-consistent end-effector behavior. The authors also developed a lightweight quadruped mobile manipulation platform for validation, and real-world experiments successfully deployed on complex terrains including slopes up to 30 degrees, stairs, and deformable terrain.