HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning

Humanoid robots promise whole-body interaction in human-centered environments, but scalable policy learning remains difficult because task-level semantic reasoning and whole-body level dynamic execution are tightly coupled. A practical solution is hierarchical control, where a high-level policy predicts intermediate whole-body actions and low-level general motion trackers (GMTs) execute them as stable humanoid motion. However, existing benchmarks rarely evaluate the policy–tracker interface itself, leaving open whether intermediate whole-body actions are executable, robust under task distribution shifts, and transferable across different GMT backends. We introduce \textsc{HumanoidArena}, a simulation-first benchmark for egocentric hierarchical whole-body learning. The benchmark formulates policy learning as a hierarchical decision making problem: a high-level policy converts egocentric vision, proprioception, and instructions into a compact whole-body action, which is subsequently executed by a low-level GMT. Instead of treating the legs as planar transport tools, \textsc{HumanoidArena} emphasizes interactions where lower-body coordination is structurally necessary in task completion. We therefore design 7 leg-critical HOI/HSI tasks in which success requires foot placement, balance maintenance, posture adjustment, and whole-body reorientation. To further diagnose the hierarchical system, we evaluate policies from two complementary perspectives: perturbation-conditioned generalization and GMT-conditioned transfer. We benchmark representative imitation-learning and VLA-style policies under this shared interface. Experiments show that hierarchical control enables learned policies to solve diverse leg-critical interactions, but performance is strongly tracker-conditioned and cross-GMT transfer remains fragile. These results position \textsc{HumanoidArena} as a benchmark for studying transferable intermediate action representations and scalable egocentric whole-body policy learning. Code and data are available at: \url{https://anonymous.4open.science/r/HumanoidArena-D36F}.