Imitation Learning (IL) holds great promise for enabling agile locomotion inembodied agents. However, many existing locomotion benchmarks primarilyfocus on simplified toy tasks, often failing to capture the complexity of real-worldscenarios and steering research toward unrealistic domains. To advance research inIL for locomotion, we present a novel benchmark designed to facilitate rigorousevaluation and comparison of IL algorithms. This benchmark encompasses adiverse set of environments, including quadrupeds, bipeds, and musculoskeletalhuman models, each accompanied by comprehensive datasets, such as real noisymotion capture data, ground truth expert data, and ground truth sub-optimal data,enabling evaluation across a spectrum of difficulty levels. To increase the robustnessof learned agents, we provide an easy interface for dynamics randomization andoffer a wide range of partially observable tasks to train agents across differentembodiments. Finally, we provide handcrafted metrics for each task and ship ourbenchmark with state-of-the-art baseline algorithms to ease evaluation and enablefast benchmarking.