# LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion

arXiv: https://arxiv.org/abs/2311.02496 · HTML: https://mlsanity.com/paper/2311.02496/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Placed in Real-world robot offline RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Placed in

- Offline RL and imitation learning from data / [Real-world robot offline RL](https://mlsanity.com/offlinerl/real-world-robot-offline-rl/)

## Builds on (most cited first, 15 of 15)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning](https://mlsanity.com/paper/1910.11956/) (2019)
- [dm_control: Software and Tasks for Continuous Control](https://mlsanity.com/paper/2006.12983/) (2020)
- [IQ-Learn: Inverse soft-Q Learning for Imitation](https://mlsanity.com/paper/2106.12142/) (2021)
- [Generative Adversarial Imitation from Observation](https://mlsanity.com/paper/1807.06158/) (2018)
- [Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow](https://mlsanity.com/paper/1810.00821/) (2018)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [MushroomRL: Simplifying Reinforcement Learning Research](https://mlsanity.com/paper/2001.01102/) (2020)
- [LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning](https://mlsanity.com/paper/2303.00599/) (2023)
- [Simitate: A Hybrid Imitation Learning Benchmark](https://mlsanity.com/paper/1905.06002/) (2019)

## Built on by (most cited first, 30 of 33)

- [HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation](https://mlsanity.com/paper/2403.10506/) (2024)
- [LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction](https://mlsanity.com/paper/2506.13751/) (2025)
- [BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark](https://mlsanity.com/paper/2407.07788/) (2024)
- [Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture](https://mlsanity.com/paper/2402.17192/) (2024)
- [Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking](https://mlsanity.com/paper/2412.17730/) (2024)
- [Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control](https://mlsanity.com/paper/2502.12724/) (2025)
- [Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks](https://mlsanity.com/paper/2507.23172/) (2025)
- [SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending](https://mlsanity.com/paper/2506.09366/) (2025)
- [Differentiable Biomechanics for Markerless Motion Capture in Upper Limb Stroke Rehabilitation: A Comparison with Optical Motion Capture](https://mlsanity.com/paper/2411.14992/) (2024)
- [Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone](https://mlsanity.com/paper/2507.08268/) (2025)
- [TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot Control](https://mlsanity.com/paper/2502.17322/) (2025)
- [Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation](https://mlsanity.com/paper/2412.03949/) (2024)
- [NIL: No-data Imitation Learning by Leveraging Pre-trained Video Diffusion Models](https://mlsanity.com/paper/2503.10626/) (2025)
- [Efficient Imitation under Misspecification](https://mlsanity.com/paper/2503.13162/) (2025)
- [KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture](https://mlsanity.com/paper/2505.13436/) (2025)
- [Monocular Biomechanical Tracking of Fingers with Inverse Kinematics to Foundation Models](https://mlsanity.com/paper/2605.09258/) (2026)
- [HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning](https://mlsanity.com/paper/2606.08610/) (2026)
- [Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation](https://mlsanity.com/paper/2402.12676/) (2024)
- [TeleOpBench: A Simulator-Centric Benchmark for Dual-Arm Dexterous Teleoperation](https://mlsanity.com/paper/2505.12748/) (2025)
- [RILe: Reinforced Imitation Learning](https://mlsanity.com/paper/2406.08472/) (2024)
- [Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal Humanoid Locomotion](https://mlsanity.com/paper/2407.11658/) (2024)
- [The Role of Domain Randomization in Training Diffusion Policies for Whole-Body Humanoid Control](https://mlsanity.com/paper/2411.01349/) (2024)
- [Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking](https://mlsanity.com/paper/2606.08253/) (2026)
- [WOLF-VLA: Whole-Body Humanoid Optimal Locomotion Framework for Vision-Language-Action Learning](https://mlsanity.com/paper/2606.25591/) (2026)
- [Latent Action Priors for Locomotion with Deep Reinforcement Learning](https://mlsanity.com/paper/2410.03246/) (2024)
- [Unpacking the Individual Components of Diffusion Policy](https://mlsanity.com/paper/2412.00084/) (2024)
- [BiomechGPT: Extending Motion-Language Models to Clinical Motion Understanding](https://mlsanity.com/paper/2505.18465/) (2025)
- [Discrete Variational Autoencoding via Policy Search](https://mlsanity.com/paper/2509.24716/) (2025)
- [Imitation Learning from Human Motion Alone Does Not Guarantee Biomechanically Plausible Gait Kinetics](https://mlsanity.com/paper/2603.12408/) (2026)
- [Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updates](https://mlsanity.com/paper/2605.11020/) (2026)

## Abstract (arXiv)

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.