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This paper introduces LUCID, a hierarchical model-based reinforcement learning framework for long-horizon humanoid loco-manipulation. It learns reusable latent skills and a macro-dynamics world model, enabling high-level planning via imagined rollouts and improving success rates in simulated multi-object rearrangement tasks.
OASIS is a simulation-data-driven framework for humanoid loco-manipulation that uses 3D generative models and hierarchical visuomotor policies. It achieves better zero-shot performance than real-robot training by leveraging domain randomization in simulation.
GRAIL generates diverse humanoid manipulation and locomotion data using 3D assets and video foundation models, enabling effective sim-to-real transfer for humanoid robot control with high real-world success rates.