🤖 Humanoid 🦾 Industrial & Cobot 🚚 AGV / AMR 🐕 Quadruped ⚙️ Reducers · Servos · Sensors 🚁 Drones & Autonomy 🧠 Embodied AI
Robos News
Robotics

Physics-Guided Biomechanical Gait Adaptation for Humanoid Locomotion on Extreme Sloped Terrains

arXiv:2607.07830v1 Announce Type: new Abstract: Model-free reinforcement learning has enabled impressive humanoid locomotion; however, control on steep slopes remains largely unexplored. Unlike flat or discrete terrains, sloped terrains impose a persistent gravitational bias that demands simultaneous stability and posture control. Consequently, under generic reward formulations, policies can converge to slow, conservative low-center-of-mass (CoM) crouched gaits. In this work, we propose a nov

Published July 10, 2026 · Category: Robotics

Overview

arXiv:2607.07830v1 Announce Type: new Abstract: Model-free reinforcement learning has enabled impressive humanoid locomotion; however, control on steep slopes remains largely unexplored. Unlike flat or discrete terrains, sloped terrains impose a persistent gravitational bias that demands simultaneous stability and posture control. Consequently, under generic reward formulations, policies can converge to slow, conservative low-center-of-mass (CoM) crouched gaits. In this work, we propose a novel two-stage physics-guided framework, dubbed HumoSlope, dedicated to robust humanoid locomotion on diverse sloped terrains. Specifically, Stage I establishes a terrain-consistent balance prior by introducing a slope-adaptive Zero Moment Point (ZMP) regularizer evaluated directly on the local inclined support plane rather than a world-horizontal reference. To prevent the resulting policy from defaulting to a crouched posture, Stage II introduces the Biomechanical Slope Gait Adapter (BSGA). Utilizing extracted macroscopic terrain descriptors as privileged, training-only signals, BSGA dynamically gates soft reward priors to modulate CoM height and lower-limb coordination based on the estimated slope geometry -- encouraging hip-dominant uphill propulsion and knee-oriented downhill braking. Crucially, the deployed actor remains entirely proprioceptive, requiring no online exteroceptive sensing. Extensive Sim-to-Real experiments demonstrate that our framework effectively mitigates posture degeneration and enables blind, continuous traversal of outdoor grass slopes up to 62.7% ($32.1^\circ$), validating a physics-guided approach to challenging slope terrain adaptation.

Source

Originally published at arxiv.org.

Related Articles

CD
Robos News Newsroom

Robos News covers markets, crypto and commodities for Asia & the Middle East — tier-1 desk research, AI-driven analysis, institutional-grade data. Tip our newsroom: [email protected]

Email the newsroom →
Disclaimer: This article is for informational purposes only and does not constitute investment advice. Data may be delayed up to 15 minutes. Past performance is not indicative of future results. Consult a licensed financial advisor before making investment decisions.

Related Stories

More from News →