Hierarchical control improves quadruped robot balance on rough terrain
Learning Structured Locomotion References for Optimization-Based Whole-Body Control
Robotics
Summary
Controlling four-legged robots so they walk smoothly is hard because the robot must move its body, place its feet correctly, and keep balance all at once. The authors created a control system that uses both smart planning and learning to help the robot adjust its walking when pushed or disturbed. Their system keeps a basic walking motion but learns when and how to change steps and timing in tricky situations. Tests showed this approach helped the robot stay upright more often than older methods. The results show combining learning with careful control planning can make robot walking more reliable.
What this means in practice
- •For robotics developers: Develop control systems for four-legged robots to adaptively stabilize walking on uneven or disturbed terrain.
- •For agricultural automation teams: Deploy quadruped robots with adaptive walking controllers to navigate complex farm environments with variable ground conditions.
Authors
Ömer Tarık Banuş, Uluç Saranlı, M. Mert Ankaralı
Abstract
Quadruped locomotion requires coordinating body motion,contact forces, foothold placement, and gait timing. We present a hierarchical controller in which reinforcement learning adapts these structured locomotion references while a gait scheduler, reactive footstep planner, and whole-body impulse controller retain responsibility for high-frequency execution. The model-based backbone can sustain a nominal trot independently, allowing the policy to focus on when and how to modify an already functional locomotion controller. We train the complete hierarchy in a vectorized controller-in-the-loop environment using a six-direction disturbance curriculum. Under a common simulator and evaluation protocol, the proposed controller achieves 77.29% mean survival across the tested disturbance grid, compared with 50.35% for the model-based reference controller. Training-time channel ablations identify adaptive foothold placement as the largest marginal contributor to grid survival and reveal direction-dependent effects of gait-rate adaptation. Together, these results demonstrate the viability of the proposed hierarchical controller architecture for integrating learned adaptation with optimization-based whole-body control through structured locomotion references.