mlsanityData to 14 Oct 2026

SMPLOlympics: Sports Environments for Physically Simulated Humanoids

arXiv 2407.00187 ↗First posted 28 Jun 2024cs.RO cs.CV cs.GRcited by 29

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Abstract (arXiv)

We present SMPLOlympics, a collection of physically simulated environments that allow humanoids to compete in a variety of Olympic sports. Sports simulation offers a rich and standardized testing ground for evaluating and improving the capabilities of learning algorithms due to the diversity and physically demanding nature of athletic activities. As humans have been competing in these sports for many years, there is also a plethora of existing knowledge on the preferred strategy to achieve better performance. To leverage these existing human demonstrations from videos and motion capture, we design our humanoid to be compatible with the widely-used SMPL and SMPL-X human models from the vision and graphics community. We provide a suite of individual sports environments, including golf, javelin throw, high jump, long jump, and hurdling, as well as competitive sports, including both 1v1 and 2v2 games such as table tennis, tennis, fencing, boxing, soccer, and basketball. Our analysis shows that combining strong motion priors with simple rewards can result in human-like behavior in various sports. By providing a unified sports benchmark and baseline implementation of state and reward designs, we hope that SMPLOlympics can help the control and animation communities achieve human-like and performant behaviors.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 26 of its 35 references that are in the map. First posted on arXiv 28 Jun 2024.

Builds on

26 of 35 references.

References · 26

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,612
  3. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,236
  4. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
  5. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  6. 2016Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Imagecited by 695
  7. 2023Eureka: Human-Level Reward Design via Coding Large Language Modelscited by 571
  8. 2020dm_control: Software and Tasks for Continuous Controlcited by 375
  9. 2023Perpetual Humanoid Control for Real-time Simulated Avatarscited by 218
  10. 2018DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skillscited by 163
  11. 2023Decoupling Human and Camera Motion from Videos in the Wildcited by 142
  12. 2023Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusioncited by 138
14 more
  1. 2023Universal Humanoid Motion Representations for Physics-Based Controlcited by 116
  2. 2024HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulationcited by 112
  3. 2024TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videoscited by 106
  4. 2018Neural probabilistic motor primitives for humanoid controlcited by 97
  5. 2023CALM: Conditional Adversarial Latent Models for Directable Virtual Characterscited by 94
  6. 2021From Motor Control to Team Play in Simulated Humanoid Footballcited by 87
  7. 2023PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interactioncited by 76
  8. 2018Latent Space Policies for Hierarchical Reinforcement Learningcited by 67
  9. 2023C·ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characterscited by 55
  10. 2023PMP: Learning to Physically Interact with Environments using Part-wise Motion Priorscited by 38
  11. 2023Neural Categorical Priors for Physics-Based Character Controlcited by 38
  12. 2023Composite Motion Learning with Task Controlcited by 28
  13. 2021Learning Transferable Motor Skills with Hierarchical Latent Mixture Policiescited by 26
  14. 2021Discovering Diverse Athletic Jumping Strategiescited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 14; 2026: 13.

29 papers.

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  1. 2025ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skillscited by 130
  2. 2025InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactionscited by 72
  3. 2025ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learningcited by 66
  4. 2025Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Modelscited by 56
  5. 2025LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instructioncited by 43
  6. 2025InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generationcited by 32
  7. 2024SkillMimic: Learning Basketball Interaction Skills from Demonstrationscited by 30
  8. 2025DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusioncited by 15
  9. 2025Bridging the Sim-to-Real Gap for Athletic Loco-Manipulationcited by 13
  10. 2025Human-Object Interaction via Automatically Designed VLM-Guided Motion Policycited by 12
  11. 2026InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactionscited by 11
  12. 2024A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motionscited by 5
17 more
  1. 2026TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Sizecited by 5
  2. 2026Heracles: Bridging Precise Tracking and Generative Synthesis for General Humanoid Controlcited by 5
  3. 2025VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Playcited by 4
  4. 2025Half-Physics: Enabling Kinematic 3D Human Model with Physical Interactionscited by 4
  5. 2026HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planningcited by 4
  6. 2025Task Tokens: A Flexible Approach to Adapting Behavior Foundation Modelscited by 3
  7. 2026Learning Human-Like Badminton Skills for Humanoid Robotscited by 3
  8. 2025FLAM: Foundation Model-Based Body Stabilization for Humanoid Locomotion and Manipulationcited by 2
  9. 2025Feature-Based vs. GAN-Based Learning from Demonstrations: When and Whycited by 2
  10. 2026HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactionscited by 2
  11. 2026ETCH-X: Robustify Expressive Body Fitting to Clothed Humans with Composable Datasetscited by 2
  12. 2026DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitationcited by 1
  13. 2026WOLF-VLA: Whole-Body Humanoid Optimal Locomotion Framework for Vision-Language-Action Learningcited by 1
  14. 2026SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergycited by 0
  15. 2026TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutescited by 0
  16. 2026Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Trackingcited by 0
  17. 2026MASkillBlender: Decentralized Whole-Body Coordination for Multi-Humanoid Loco-Manipulation via Skill Blendingcited by 0