PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion

arXiv 2510.18348 ↗First posted 21 Oct 2025cs.RO cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait priors that constrain the action space, bias policy optimization, and limit adaptability across robot morphologies, or (ii) operate "blind," making them unable to anticipate hind-leg terrain and brittle to observation noise. We propose Phase-Guided Terrain Traversal (PGTT), a perception-aware deep-RL approach that enforces gait structure through reward shaping, thereby reducing inductive bias compared to oscillator- or IK-conditioned action priors. PGTT encodes per-leg phase as a cubic Hermite spline, adapts swing height to local heightmap statistics, and adds a swing-phase contact penalty, while the policy acts directly in joint space for morphology-agnostic deployment. Trained in MuJoCo (MJX) on procedurally generated stair-like terrains with curriculum learning and domain randomization, PGTT achieves the highest success rate among the evaluated baselines under push disturbances (median +7.5% over the next-best baseline) and on discrete obstacles (+9%), while maintaining comparable velocity tracking. We validate PGTT on a Unitree Go2 using a real-time LiDAR elevation-to-heightmap pipeline and report preliminary results on ANYmal-C using the same hyperparameters. These results provide early evidence that terrain-adaptive, phase-guided reward shaping can transfer across platforms without platform-specific policy priors or extensive re-tuning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 27 references that are in the map. First posted on arXiv 21 Oct 2025.

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References · 18

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2017Asymmetric Actor Critic for Image-Based Robot Learningcited by 238
2022Legged Locomotion in Challenging Terrains using Egocentric Visioncited by 151
2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
2025MuJoCo Playgroundcited by 78
2023DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learningcited by 66

6 more

2020RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Controlcited by 59
2022CPG-RL: Learning Central Pattern Generators for Quadruped Locomotioncited by 34
2022Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controllercited by 29
2025Parkour in the Wild: Learning a General and Extensible Agile Locomotion Policy Using Multi-expert Distillation and RL Fine-tuningcited by 26
2022Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Locomotioncited by 14
2022Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotioncited by 12

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