[Submitted on 21 Oct 2025 (v1), last revised 22 Jul 2026 (this version, v2)]

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Abstract: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.

Submission history

From: Konstantinos Chatzilygeroudis [view email]
[v1] Tue, 21 Oct 2025 07:00:18 UTC (20,540 KB)
[v2] Wed, 22 Jul 2026 11:33:12 UTC (20,494 KB)