# Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach

arXiv: https://arxiv.org/abs/2010.16342 · HTML: https://mlsanity.com/paper/2010.16342/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model](https://mlsanity.com/paper/2104.09771/) (2021)
- [Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion](https://mlsanity.com/paper/2109.06409/) (2021)
- [BiRoDiff: Diffusion policies for bipedal robot locomotion on unseen terrains](https://mlsanity.com/paper/2407.05424/) (2024)
- [Learning Linear Policies for Robust Bipedal Locomotion on Terrains with Varying Slopes](https://mlsanity.com/paper/2104.01662/) (2021)
- [A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control](https://mlsanity.com/paper/2203.03391/) (2022)
- [Policy Search using Dynamic Mirror Descent MPC for Model Free Off Policy RL](https://mlsanity.com/paper/2110.12239/) (2021)

## Abstract (arXiv)

In this paper, with a view toward fast deployment of locomotion gaits in low-cost hardware, we use a linear policy for realizing end-foot trajectories in the quadruped robot, Stoch 2. In particular, the parameters of the end-foot trajectories are shaped via a linear feedback policy that takes the torso orientation and the terrain slope as inputs. The corresponding desired joint angles are obtained via an inverse kinematics solver and tracked via a PID control law. Augmented Random Search, a model-free and a gradient-free learning algorithm is used to train this linear policy. Simulation results show that the resulting walking is robust to terrain slope variations and external pushes. This methodology is not only computationally light-weight but also uses minimal sensing and actuation capabilities in the robot, thereby justifying the approach.