SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation

• URL: http://arxiv.org/abs/2606.08278v1
• Date: Sat, 06 Jun 2026 17:55:43 GMT
• Title: SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
• Abstract summary: We present SIMPLE, a unified simulation testbed for humanoid policy learning and evaluation.<n> SIMPLE couples the accurate contact-rich dynamics of MuJoCo with the rendering of IsaacSim.<n>It provides a large-scale environment comprising 60 diverse whole-body tasks, 50 indoor scenes, and over 1,000 object assets.
• Score: 33.50539650112054
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Humanoid foundation models are advancing faster than we can evaluate them. While real-world testing is expensive and difficult to reproduce, existing simulation benchmarks focus primarily on table-top or wheeled robots. A scalable and reproducible benchmark for whole-body humanoid loco-manipulation remains an open problem. To this end, we present SIMPLE, a unified simulation testbed for humanoid policy learning and evaluation. SIMPLE couples the accurate contact-rich dynamics of MuJoCo with the photorealistic rendering of IsaacSim. It provides a large-scale environment comprising 60 diverse whole-body tasks, 50 indoor scenes, and over 1,000 object assets. To facilitate scalable data collection, the framework integrates two data generation pipelines: automated trajectory generation via motion planning and a low-latency VR teleoperation interface. We further integrate and benchmark mainstream humanoid policies at scale in SIMPLE, including lightweight imitation networks, large vision-language-action (VLA) models, and recent world action models (WAMs). Our experiments reveal a strong correlation between policy performance in simulation and the real world. Furthermore, we demonstrate that policies trained on data collected in SIMPLE can be transferred zero-shot to physical humanoid robots under similar settings, providing a robust and reproducible foundation for humanoid robotics research.

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