Machines 2026, 14(6), 621; https://doi.org/10.3390/machines14060621 (registering DOI) - 30 May 2026
Abstract
This paper proposes LA-PPO, an improved Proximal Policy Optimization algorithm for quadruped robot locomotion control on mixed terrain. To address partial observability, temporal dependence in contact states, and non-uniform importance of historical information in complex-terrain quadruped locomotion, LA-PPO integrates Long Short-Term Memory (LSTM)
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This paper proposes LA-PPO, an improved Proximal Policy Optimization algorithm for quadruped robot locomotion control on mixed terrain. To address partial observability, temporal dependence in contact states, and non-uniform importance of historical information in complex-terrain quadruped locomotion, LA-PPO integrates Long Short-Term Memory (LSTM) and Multi-Head Attention (MHA) within an Actor–Critic framework. The LSTM module models temporal dependencies in historical observations, while the MHA module adaptively emphasizes historical information most relevant to the current action decision. Based on IsaacGym, we construct a mixed-terrain environment consisting of flat regions, sloped regions, and random rough-terrain regions and conduct algorithmic comparisons, statistics over multiple random seeds, reward component ablation studies, and attention mechanism analyses for both walking and trotting gaits. Simulation results show that LA-PPO achieves the highest final reward and the longest mean episode length in both gaits. Compared with the PPO baseline, the final reward and mean episode length are improved by approximately 42.3% and 42.7%, respectively, in the walking task, and by approximately 39.8% and 25.7%, respectively, in the trotting task. Real-robot tests further show that the learned policy can perform walking and trotting on flat ground, sloped terrain, and random rough terrain, demonstrating preliminary sim-to-real transfer capability.
Full article
(This article belongs to the Special Issue Embodied AI in Robotics)
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