Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
This paper presents a method for training reference-guided, perceptive reinforcement learning locomotion policies for humanoid robots, where reference trajectories are modulated during training to be consistent with terrain geometry. By synthesizing SE(2)-controllable reference trajectories inside the RL training loop, projecting footsteps onto valid footholds and adjusting swing-foot and center-of-mass trajectories, the resulting policy provides a clean SE(2) velocity interface. Simulation shows improved tracking performance. On hardware, integrated with an MPC and control barrier function planner, the method enables long-horizon (>70m) autonomous navigation on the Unitree G1 through rough terrain and stairs, with onboard sensing and computation.
[2605.15517] Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
[Submitted on 15 May 2026]
Title:Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
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Abstract:We present a method for training reference-guided, perceptive reinforcement learning locomotion policies for humanoid robots in which reference trajectories are modulated in training to be consistent with terrain geometry. Aiming to deploy our method with standard navigation autonomy infrastructure, we synthesize SE(2)-controllable reference trajectories inside the RL training loop, projecting desired footsteps onto valid footholds and adjusting swing-foot and center-of-mass trajectories to match the terrain. The resulting policy exposes a clean SE(2) velocity interface compatible with standard navigation planners. In simulation, environmentally-conditioned references significantly improve reference tracking performance compared to environment agnostic references. On hardware, we integrate the policy with an MPC + control barrier function planner and demonstrate long-horizon (>70m) closed-loop autonomous navigation on the Unitree G1 through outdoor environments containing rough terrain and consecutive flights of stairs, with all sensing and computation onboard.
Comments: 8 pages, 4 figures, intended to submit to Humanoids 2026
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2605.15517 [cs.RO]
(or arXiv:2605.15517v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.15517
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: William Compton IIi [view email] [v1] Fri, 15 May 2026 01:27:50 UTC (22,583 KB)
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