Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents. However, many existing locomotion benchmarks primarily focus on simplified toy tasks, often failing to capture the complexity of real-world scenarios and steering research toward unrealistic domains. To advance research in IL for locomotion, we present a novel benchmark designed to facilitate rigorous evaluation and comparison of IL algorithms. This benchmark encompasses a diverse set of environments, including quadrupeds, bipeds, and musculoskeletal human models, each accompanied by comprehensive datasets, such as real noisy motion capture data, ground truth expert data, and ground truth sub-optimal data, enabling evaluation across a spectrum of difficulty levels. To increase the robustness of learned agents, we provide an easy interface for dynamics randomization and offer a wide range of partially observable tasks to train agents across different embodiments. Finally, we provide handcrafted metrics for each task and ship our benchmark with state-of-the-art baseline algorithms to ease evaluation and enable fast benchmarking.

翻译：模仿学习在实现具身智能体的敏捷运动控制方面具有巨大潜力。然而，现有运动控制基准多聚焦于简化玩具任务，往往无法捕捉真实场景的复杂性，导致研究偏离实际领域。为推进面向运动控制的模仿学习研究，我们提出一项新基准，旨在严格评估与比较模仿学习算法。该基准涵盖多种环境，包括四足机器人、双足机器人及肌肉骨骼人体模型，并配备综合性数据集，例如真实噪声动作捕捉数据、真实专家级数据和真实次优数据，可支持跨难度梯度的算法评估。为增强习得智能体的鲁棒性，我们提供便捷的动态随机化接口，并设计多种部分可观测任务以训练不同形态智能体。最后，我们为每项任务提供自定义评估指标，并集成最新基线算法，以简化评估流程并支持快速基准测试。