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The paper introduces a method for extracting topological necessities as mechanism-invariant subgoals from offline trajectories, enabling cross-embodiment transfer in goal-conditioned reinforcement learning, with empirical improvements on benchmark tasks.
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.