# Achieving Human Level Competitive Robot Table Tennis

arXiv: https://arxiv.org/abs/2408.03906 · HTML: https://mlsanity.com/paper/2408.03906/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2024.

## Built on by (most cited first, 27 of 27)

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- [LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos](https://mlsanity.com/paper/2503.20936/) (2025)
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- [Towards Ball Spin and Trajectory Analysis in Table Tennis Broadcast Videos via Physically Grounded Synthetic-to-Real Transfer](https://mlsanity.com/paper/2504.19863/) (2025)
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- [Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum](https://mlsanity.com/paper/2511.11218/) (2025)
- [PACE: Physics Augmentation for Coordinated End-to-end Reinforcement Learning toward Versatile Humanoid Table Tennis](https://mlsanity.com/paper/2509.21690/) (2025)
- [ECPv2: Fast, Efficient, and Scalable Global Optimization of Lipschitz Functions](https://mlsanity.com/paper/2511.16575/) (2025)
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- [Energy-Based Transfer for Reinforcement Learning](https://mlsanity.com/paper/2506.16590/) (2025)
- [BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking](https://mlsanity.com/paper/2509.18387/) (2025)
- [TEMPO: Learning Temporal Context for Dynamic Robot Manipulation](https://mlsanity.com/paper/2609.16864/) (2026)
- [SpikePingpong: Spike Vision-based Fast-Slow Pingpong Robot System](https://mlsanity.com/paper/2506.06690/) (2025)
- [How good was my shot? Quantifying Player Skill Level in Table Tennis](https://mlsanity.com/paper/2603.25736/) (2026)
- [1000 Rallies: An Event-Camera Dataset and Real-Time Learned Ball-State Estimation for Robotic Table Tennis](https://mlsanity.com/paper/2606.25620/) (2026)
- [PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation](https://mlsanity.com/paper/2607.01938/) (2026)
- [Chess on Ice: Curling Tactical Decision-Making via Backward Induction and Deep Reinforcement Learning](https://mlsanity.com/paper/2608.02379/) (2026)
- [SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning](https://mlsanity.com/paper/2608.09138/) (2026)
- [Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking](https://mlsanity.com/paper/2608.20087/) (2026)

## Abstract (arXiv)

Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a physically demanding sport which requires human players to undergo years of training to achieve an advanced level of proficiency. In this paper, we contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their detailed skill descriptors which model the agent's capabilities and help to bridge the sim-to-real gap and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real including an iterative approach to defining the task distribution that is grounded in the real-world and defines an automatic curriculum, and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100% matches vs. beginners and 55% matches vs. intermediate players, demonstrating solidly amateur human-level performance. Videos of the matches can be viewed at https://sites.google.com/view/competitive-robot-table-tennis