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LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
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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.
Forward citations
Cited by 6 Pith papers
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First Deployable Dynamic-CoM: A Unified Policy and Method-Agnostic Benchmark for Humanoid Single-Leg Balance
A support-relative dynamic capture-point observation, reconstructible without base linear velocity, lets a humanoid policy hold clean single-leg balance at 86/90 in simulation and deploy on a Unitree G1 without distillation.
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WOLF-VLA: Whole-Body Humanoid Optimal Locomotion Framework for Vision-Language-Action Learning
WOLF-VLA combines optimal-control motion synthesis with multi-modal dataset construction to train VLAs that generate whole-body humanoid locomotion policies from natural-language instructions.
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Distinguishing Imitation Error from Intrinsic Motion Learning Difficulty
A physics-based score (MDS) predicts how hard a motion is for a humanoid to imitate by measuring how much joint torques must change under small pose perturbations.
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PHUMA: Physically Reliable Humanoid Locomotion Dataset
PHUMA is a curated 73-hour humanoid locomotion corpus whose physical-reliability metrics are partly defined by the same losses used to optimize it, and whose imitation success claims are confounded by in-distribution ...
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WOLF-VLA: Whole-Body Humanoid Optimal Locomotion Framework for Vision-Language-Action Learning
WOLF-VLA creates a dataset of optimal-control humanoid trajectories and trains a VLA model to generate locomotion policies from natural language instructions, with planned open release of data and tools.
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Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kinetics
Motion-only imitation learning reproduces walking kinematics but produces inaccurate ground reaction forces and joint moments; adding GRF and center-of-pressure rewards brings simulated kinetics closer to inverse dynamics.
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