Creates the first large event-camera dataset for table tennis and uses a CNN to estimate ball state, achieving 36% better bounce prediction and enabling real-time robot rallies.
An event-based perception pipeline for a table tennis robot
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Event-based perception combined with progressive low-to-high speed training improves robotic table tennis return accuracy by 35.8% using the same number of training episodes.
citing papers explorer
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1000 Rallies: An Event-Camera Dataset and Real-Time Learned Ball-State Estimation for Robotic Table Tennis
Creates the first large event-camera dataset for table tennis and uses a CNN to estimate ball state, achieving 36% better bounce prediction and enabling real-time robot rallies.
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Biologically Inspired Event-Based Perception and Sample-Efficient Learning for High-Speed Table Tennis Robots
Event-based perception combined with progressive low-to-high speed training improves robotic table tennis return accuracy by 35.8% using the same number of training episodes.