HumanoidArena: 以自我为中心的层级全身学习基准
HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
AI总结：
提出HumanoidArena基准，通过层级控制（高层策略输出全身动作，低层通用运动跟踪器执行）解决人形机器人全身交互学习问题，设计7个腿部关键任务评估策略的泛化与迁移能力。
摘要：
人形机器人有望在人类中心环境中实现全身交互，但由于任务级决策与全身动态执行紧密耦合，可扩展的策略学习仍然困难。一个实用的解决方案是层级控制，其中高层策略预测中间全身动作，低层通用运动跟踪器（GMT）将其执行为稳定的人形运动。然而，现有基准很少评估策略-跟踪器接口本身，因此尚不清楚中间全身动作是否可执行、在任务分布变化下是否鲁棒以及是否可跨不同GMT后端迁移。我们引入HumanoidArena，一个以自我为中心的层级全身学习的仿真优先基准。该基准将策略学习形式化为一个层级决策问题：高层策略将自我中心视觉、本体感觉和指令转换为紧凑的全身动作，随后由低层GMT执行。HumanoidArena不将腿部视为平面运输工具，而是强调下肢协调在任务完成中结构上必要的交互。因此，我们设计了7个腿部关键的人-物交互/人-场景交互（HOI/HSI）任务，其中成功需要足部放置、平衡维持、姿势调整和全身重新定向。为了进一步诊断层级系统，我们从两个互补角度评估策略：扰动条件泛化和GMT条件迁移。实验表明，层级控制使学习策略能够解决多样的腿部关键交互，但性能强烈依赖于跟踪器，且跨GMT迁移仍然脆弱。这些结果使HumanoidArena成为研究可迁移中间动作表示和可扩展的自我中心全身策略学习的基准。
英文摘要：
Humanoid robots promise whole-body interaction in human-centered environments, but scalable policy learning remains difficult because task-level decision-making and whole-body 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 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, 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. 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 HumanoidArena as a benchmark for studying transferable intermediate action representations and scalable egocentric whole-body policy learning.
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