# MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds

arXiv: https://arxiv.org/abs/2606.10288 · HTML: https://mlsanity.com/paper/2606.10288/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [Legged Locomotion in Challenging Terrains using Egocentric Vision](https://mlsanity.com/paper/2211.07638/) (2022)
- [Humanoid Parkour Learning](https://mlsanity.com/paper/2406.10759/) (2024)
- [Perceptive Locomotion through Nonlinear Model Predictive Control](https://mlsanity.com/paper/2208.08373/) (2022)
- [mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning](https://mlsanity.com/paper/2601.22074/) (2026)
- [Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control](https://mlsanity.com/paper/2011.00032/) (2020)
- [Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains](https://mlsanity.com/paper/2511.14625/) (2025)
- [Sim-to-Real Learning of Footstep-Constrained Bipedal Dynamic Walking](https://mlsanity.com/paper/2203.07589/) (2022)
- [Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids](https://mlsanity.com/paper/2601.07718/) (2026)
- [Learning Perceptive Humanoid Locomotion over Challenging Terrain](https://mlsanity.com/paper/2503.00692/) (2025)
- [DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction](https://mlsanity.com/paper/2510.07152/) (2025)
- [RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains](https://mlsanity.com/paper/2602.03002/) (2026)
- [3D Underactuated Bipedal Walking via H-LIP based Gait Synthesis and Stepping Stabilization](https://mlsanity.com/paper/2101.09588/) (2021)
- [Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety](https://mlsanity.com/paper/2105.01697/) (2021)
- [Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds](https://mlsanity.com/paper/2601.06286/) (2026)
- [CLF-RL: Control Lyapunov Function Guided Reinforcement Learning](https://mlsanity.com/paper/2508.09354/) (2025)
- [Chasing Autonomy: Dynamic Retargeting and Control Guided RL for Performant and Controllable Humanoid Running](https://mlsanity.com/paper/2603.25902/) (2026)
- [NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning](https://mlsanity.com/paper/2510.11542/) (2025)

## Built on by (most cited first, 1 of 1)

- [STRIDER: Stepping-Enabled Multi-Gait Hierarchical 3D Loco-Manipulation Framework for Humanoid Robots](https://mlsanity.com/paper/2609.23483/) (2026)

## Abstract (arXiv)

Perceptive bipedal locomotion over sparse terrain remains a difficult challenge: model-based methods are precise but brittle to uncertainty, while model-free methods are robust but struggle to discover the precise, constrained motions required for safety-critical locomotion where small errors can cause catastrophic failures. We propose a model-assisted reinforcement learning (RL) framework that combines both perspectives in three steps: (1) generate a safe reference trajectory using simplified models; (2) train a privileged teacher policy guided by a control Lyapunov function (CLF) reward built around the safe reference trajectory; and (3) distill the teacher into a vision-based student policy. We show that this model-assistance procedure produces physically grounded locomotion, improving sample efficiency, reducing the need for a complex learning curriculum, and achieving smoother locomotion behavior alongside stepping stone performance comparable to model-free baselines. We validate our approach in simulation and demonstrate successful deployment on a Unitree G1 humanoid robot navigating sparse footholds with lateral constraints.