# Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation

arXiv: https://arxiv.org/abs/2610.10934 · HTML: https://mlsanity.com/paper/2610.10934/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Topological Deep Learning: Going Beyond Graph Data](https://mlsanity.com/paper/2206.00606/) (2022)
- [Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks](https://mlsanity.com/paper/2304.10031/) (2023)
- [AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control](https://mlsanity.com/paper/2503.21401/) (2025)
- [Learning Fault-Tolerant Locomotion with Adaptive Gait Timing](https://mlsanity.com/paper/2608.07328/) (2026)

## Abstract (arXiv)

Actuator degradation turns quadruped locomotion into a coordination problem requiring joints to compensate for lost actuation. Prior work suggests that morphology-aware graph policies improve learning and generalization under body perturbations. We ask whether these benefits can be strengthened by explicitly modeling higher-order mechanical structure. We represent the Unitree Go1 as a cell complex with limb- and body-level rank-2 cells and apply Hodge-based message passing. Under degradation training, the node-edge-face Hodge actor achieves the highest return on unseen actuator degradations, with higher survival and lower velocity-tracking error. These results support higher-order morphology as a useful inductive bias for whole-body compensation under actuator degradation.