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WholeBodyWAM is introduced, which jointly predicts future visual dynamics, manipulation actions, and whole-body control intents for generalizable humanoid loco-manipulation and preserves pre-trained world-action priors while grounding heterogeneous whole-body controller (WBC) semantics and coordinating whole-body behavior.
Achieving up to 2.35× faster inference with only 19–28% of the original KV cache, VisCache redefines efficiency in Vision Large Language Models.
ERSkill boosts LLM performance by over 31% through dynamic, skill-guided memory retrieval that evolves with experience.
