# HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning

arXiv: https://arxiv.org/abs/2508.21043 · HTML: https://mlsanity.com/paper/2508.21043/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2025.

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

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- [Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations](https://mlsanity.com/paper/2602.06643/) (2026)
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- [Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum](https://mlsanity.com/paper/2511.11218/) (2025)
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- [Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy](https://mlsanity.com/paper/2603.01953/) (2026)
- [HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning](https://mlsanity.com/paper/2606.08610/) (2026)
- [PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball](https://mlsanity.com/paper/2607.28623/) (2026)
- [PACE: Physics Augmentation for Coordinated End-to-end Reinforcement Learning toward Versatile Humanoid Table Tennis](https://mlsanity.com/paper/2509.21690/) (2025)
- [USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots](https://mlsanity.com/paper/2510.07869/) (2025)
- [Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors](https://mlsanity.com/paper/2605.22272/) (2026)
- [HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers](https://mlsanity.com/paper/2606.06493/) (2026)
- [DADP: Domain Adaptive Diffusion Policy](https://mlsanity.com/paper/2602.04037/) (2026)
- [TT4D: A Pipeline and Dataset for Table Tennis 4D Reconstruction From Monocular Videos](https://mlsanity.com/paper/2605.01234/) (2026)
- [RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning](https://mlsanity.com/paper/2606.11092/) (2026)
- [How good was my shot? Quantifying Player Skill Level in Table Tennis](https://mlsanity.com/paper/2603.25736/) (2026)
- [Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks](https://mlsanity.com/paper/2604.09487/) (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)
- [TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes](https://mlsanity.com/paper/2606.26215/) (2026)

## Abstract (arXiv)

Humanoid robots have recently achieved impressive progress in locomotion and whole-body control, yet they remain constrained in tasks that demand rapid interaction with dynamic environments through manipulation. Table tennis exemplifies such a challenge: with ball speeds exceeding 5 m/s, players must perceive, predict, and act within sub-second reaction times, requiring both agility and precision. To address this, we present a hierarchical framework for humanoid table tennis that integrates a model-based planner for ball trajectory prediction and racket target planning with a reinforcement learning-based whole-body controller. The planner determines striking position, velocity and timing, while the controller generates coordinated arm and leg motions that mimic human strikes and maintain stability and agility across consecutive rallies. Moreover, to encourage natural movements, human motion references are incorporated during training. We validate our system on a general-purpose humanoid robot, achieving up to 106 consecutive shots with a human opponent and sustained exchanges against another humanoid. These results demonstrate real-world humanoid table tennis with sub-second reactive control, marking a step toward agile and interactive humanoid behaviors.