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SpikePingpong: Spike Vision-based Fast-Slow Pingpong Robot System

arXiv 2506.06690 ↗First posted 7 Jun 2025cs.RO cs.CVcited by 0

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Abstract (arXiv)

Learning to control high-speed objects in dynamic environments represents a fundamental challenge in robotics. Table tennis serves as an ideal testbed for advancing robotic capabilities in dynamic environments. This task presents two fundamental challenges: it requires a high-precision vision system capable of accurately predicting ball trajectories under complex dynamics, and it necessitates intelligent control strategies to ensure precise ball striking to target regions. High-speed object manipulation typically demands advanced visual perception hardware capable of capturing rapid motion with exceptional temporal resolution. Drawing inspiration from Kahneman's dual-system theory, where fast intuitive processing complements slower deliberate reasoning, there exists an opportunity to develop more robust perception architectures that can handle high-speed dynamics while maintaining accuracy. To this end, we present textitSpikePingpong, a novel system that integrates spike-based vision with imitation learning for high-precision robotic table tennis. We develop a Fast-Slow system architecture where System 1 provides rapid ball detection and preliminary trajectory prediction with millisecond-level responses, while System 2 employs spike-oriented neural calibration for precise hittable position corrections. For strategic ball striking, we introduce Imitation-based Motion Planning And Control Technology, which learns optimal robotic arm striking policies through demonstration-based learning. Experimental results demonstrate that textitSpikePingpong achieves a remarkable 92% success rate for 30 cm accuracy zones and 70% in the more challenging 20 cm precision targeting. This work demonstrates the potential of Fast-Slow architectures for advancing robotic capabilities in time-critical manipulation tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 63 references that are in the map. First posted on arXiv 7 Jun 2025.

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29 of 63 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  3. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,568
  4. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  5. 2024OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learningcited by 189
  6. 2018Learning to Walk via Deep Reinforcement Learningcited by 158
  7. 2019TossingBot: Learning to Throw Arbitrary Objects with Residual Physicscited by 137
  8. 2025ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skillscited by 130
  9. 2025HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpitcited by 76
  10. 2024WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contactscited by 61
  11. 2022SpeedFolding: Learning Efficient Bimanual Folding of Garmentscited by 52
  12. 2020Learning to Play Table Tennis From Scratch using Muscular Robotscited by 35
17 more
  1. 2023Dynamic Handover: Throw and Catch with Bimanual Handscited by 35
  2. 2023Grasp-Anything: Large-scale Grasp Dataset from Foundation Modelscited by 33
  3. 2022i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loopscited by 32
  4. 2024Achieving Human Level Competitive Robot Table Tenniscited by 27
  5. 2024UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondencecited by 23
  6. 2021Spike Camera and Its Coding Methodscited by 19
  7. 2025CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-Worldcited by 18
  8. 2024Catch It! Learning to Catch in Flight with Mobile Dexterous Handscited by 16
  9. 2020Robotic Table Tennis with Model-Free Reinforcement Learningcited by 15
  10. 2023Robotic Table Tennis: A Case Study into a High Speed Learning Systemcited by 13
  11. 2020Sample-efficient Reinforcement Learning in Robotic Table Tenniscited by 12
  12. 2022GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robotcited by 9
  13. 2023DexCatch: Learning to Catch Arbitrary Objects with Dexterous Handscited by 8
  14. 2024An Intelligent Robotic System for Perceptive Pancake Batter Stirring and Precise Pouringcited by 3
  15. 2024Preprocessing-based Kinodynamic Motion Planning Framework for Intercepting Projectiles using a Robot Manipulatorcited by 1
  16. 2025Bridging Adaptivity and Safety: Learning Agile Collision-Free Locomotion Across Varied Physicscited by 1
  17. 2025Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architecturescited by 1

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  3. 2025Learning Vision-Driven Reactive Soccer Skills for Humanoid Robots
  4. 2024Learning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidance
  5. 2025Catching Spinning Table Tennis Balls in Simulation with End-to-End Curriculum Reinforcement Learning
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  8. 2026Physics Models for Sim-to-Real Transfer in Professional-Level Robot Table Tennis ↗