mlsanityData to 13 Oct 2026

Strategy and Skill Learning for Physics-based Table Tennis Animation

arXiv 2407.16210 ↗First posted 23 Jul 2024cs.GR cs.AI cs.LGcited by 17

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Abstract (arXiv)

Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, boxing, and tennis. However, reproducing the selection and use of diverse motor skills in dynamic environments to solve complex tasks, as humans do, still remains a challenge. We present a strategy and skill learning approach for physics-based table tennis animation. Our method addresses the issue of mode collapse, where the characters do not fully utilize the motor skills they need to perform to execute complex tasks. More specifically, we demonstrate a hierarchical control system for diversified skill learning and a strategy learning framework for effective decision-making. We showcase the efficacy of our method through comparative analysis with state-of-the-art methods, demonstrating its capabilities in executing various skills for table tennis. Our strategy learning framework is validated through both agent-agent interaction and human-agent interaction in Virtual Reality, handling both competitive and cooperative tasks.

Key facts. Cited by 17 papers in the map (data through 13 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

16 of 49 references.

References · 16

  1. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 890
  2. 2018DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skillscited by 153
  3. 2018Neural probabilistic motor primitives for humanoid controlcited by 96
  4. 2023CALM: Conditional Adversarial Latent Models for Directable Virtual Characterscited by 91
  5. 2023C·ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characterscited by 53
  6. 2023Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperationcited by 49
  7. 2021Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularizationcited by 44
  8. 2023PMP: Learning to Physically Interact with Environments using Part-wise Motion Priorscited by 38
  9. 2023Neural Categorical Priors for Physics-Based Character Controlcited by 37
  10. 2023Composite Motion Learning with Task Controlcited by 26
  11. 2017Learnings Options End-to-End for Continuous Action Taskscited by 24
  12. 2022ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characterscited by 21
4 more
  1. 2023Animating Human Athleticscited by 16
  2. 2018Safe Option-Critic: Learning Safety in the Option-Critic Architecturecited by 10
  3. 2023Explainable Human-Robot Training and Cooperation with Augmented Realitycited by 6
  4. 2021Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Studycited by 5

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 10; 2026: 4.

17 papers.

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  1. 2025ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skillscited by 105
  2. 2024ExBody2: Advanced Expressive Humanoid Whole-Body Controlcited by 76
  3. 2025InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactionscited by 68
  4. 2025TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenizationcited by 48
  5. 2024Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Controlcited by 41
  6. 2025InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generationcited by 32
  7. 2025PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllerscited by 24
  8. 2025SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrationscited by 23
  9. 2025CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objectscited by 11
  10. 2026InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactionscited by 10
  11. 2025Learning to Ball: Composing Policies for Long-Horizon Basketball Movescited by 6
  12. 2025InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphscited by 4
5 more
  1. 2026TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Sizecited by 4
  2. 2025Egocentric Event-Based Vision for Ping Pong Ball Trajectory Predictioncited by 3
  3. 2026Learning Human-Like Badminton Skills for Humanoid Robotscited by 3
  4. 2026DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitationcited by 1
  5. 2024LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Realitycited by 0