الملخص
While energy efficiency is a critical objective for legged-robot locomotion control, achieving low energy consumption while maintaining robust performance across different velocity ranges and terrain conditions remains a key challenge. This is particularly true for end-to-end RL policies, where gait generation, motion execution, and energy optimization are tightly coupled, leading to high sensitivity to reward design. In this work, we propose a hierarchical reinforcement learning (HRL) framework that separates a high-frequency policy for stable and robust joint-level motion execution from low-frequency gait adaptation that explicitly minimizes the cost of transport (CoT). The three-stage Isaac-based training procedure enables zero-shot sim-to-real transfer with improved tracking accuracy, robustness, and energy efficiency. The learned hierarchy exhibits automatic speed-dependent gait adaptation, transitioning from pacing at low speeds to trotting at higher speeds. We validate the proposed approach in simulation against representative single-policy and hierarchical locomotion baselines, demonstrating reduced CoT over a broad range of commanded velocities, while maintaining robust locomotion across flat, uneven rough, and inclined terrains. We further demonstrate its practical feasibility through zero-shot deployment on a physical Unitree AlienGo quadruped.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Issa, A., Singh, A., Tsaritsin, A., & Kolyubin, S. (2026). Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains. https://omanscience.com/ar/articles/energy-efficient-gait-adaptation-via-hierarchical-reinforcement-learning-for-quadrupedal-locomotion-across-diverse-terrains
MLA 9
Issa, Ammar, et al. "Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains." https://omanscience.com/ar/articles/energy-efficient-gait-adaptation-via-hierarchical-reinforcement-learning-for-quadrupedal-locomotion-across-diverse-terrains.
شيكاغو (المؤلف–التاريخ)
Issa, Ammar, Anubhav Singh, Anton Tsaritsin, and Sergey Kolyubin. 2026. "Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains." https://omanscience.com/ar/articles/energy-efficient-gait-adaptation-via-hierarchical-reinforcement-learning-for-quadrupedal-locomotion-across-diverse-terrains.
هارفارد
Issa, A., Singh, A., Tsaritsin, A. and Kolyubin, S. (2026) 'Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains', Available at: https://omanscience.com/ar/articles/energy-efficient-gait-adaptation-via-hierarchical-reinforcement-learning-for-quadrupedal-locomotion-across-diverse-terrains.
فانكوفر
Issa A, Singh A, Tsaritsin A, Kolyubin S. Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains. https://omanscience.com/ar/articles/energy-efficient-gait-adaptation-via-hierarchical-reinforcement-learning-for-quadrupedal-locomotion-across-diverse-terrains
IEEE
A. Issa, A. Singh, A. Tsaritsin, and S. Kolyubin, "Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains," https://omanscience.com/ar/articles/energy-efficient-gait-adaptation-via-hierarchical-reinforcement-learning-for-quadrupedal-locomotion-across-diverse-terrains.