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This paper uses reinforcement learning to tune the high-level references of a model-based quadruped controller, rather than replacing that controller with an end-to-end policy. The reported gain is 77.29% versus 50.35% mean survival across a disturbance grid, in a single simulator setup, so it needs independent replication on hardware and under other protocols. For ML readers, the useful takeaway is the hybrid design: a learned policy acting on structured, interpretable control references, with adaptive foothold placement as the largest contributor.
Learning Structured Locomotion References for Optimization-Based Whole-Body Control
arXiv:2610.24779v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2610.24779 [cs.RO]
(or arXiv:2610.24779v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.24779 Focus to learn more arXiv-issued DOI via DataCite
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