# RL-augmented Adaptive Model Predictive Control for Bipedal Locomotion over Challenging Terrain

arXiv: https://arxiv.org/abs/2509.18466 · HTML: https://mlsanity.com/paper/2509.18466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [Residual MPC: Blending Reinforcement Learning with GPU-Parallelized Model Predictive Control](https://mlsanity.com/paper/2510.12717/) (2025)
- [Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation](https://mlsanity.com/paper/2606.05687/) (2026)
- [Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation](https://mlsanity.com/paper/2609.21307/) (2026)

## Abstract (arXiv)

Model predictive control (MPC) has demonstrated effectiveness for humanoid bipedal locomotion; however, its applicability in challenging environments, such as rough and slippery terrain, is limited by the difficulty of modeling terrain interactions. In contrast, reinforcement learning (RL) has achieved notable success in training robust locomotion policies over diverse terrain, yet it lacks guarantees of constraint satisfaction and often requires substantial reward shaping. Recent efforts in combining MPC and RL have shown promise of taking the best of both worlds, but they are primarily restricted to flat terrain or quadrupedal robots. In this work, we propose an RL-augmented MPC framework tailored for bipedal locomotion over rough and slippery terrain. Our method parametrizes three key components of single-rigid-body-dynamics-based MPC: system dynamics, swing leg controller, and gait frequency. We validate our approach through bipedal robot simulations in NVIDIA IsaacLab across various terrains, including stairs, stepping stones, and low-friction surfaces. Experimental results demonstrate that our RL-augmented MPC framework produces significantly more adaptive and robust behaviors compared to baseline MPC and RL.