SpikePingpong: High-Frequency Spike Vision-based Robot Learning for Precise Striking in Table Tennis Game
Journal:
arXiv
Published Date:
Jun 7, 2025
Abstract
Learning to control high-speed objects in the real world remains a
challenging frontier in robotics. Table tennis serves as an ideal testbed for
this problem, demanding both rapid interception of fast-moving balls and
precise adjustment of their trajectories. This task presents two fundamental
challenges: it requires a high-precision vision system capable of accurately
predicting ball trajectories, and it necessitates intelligent strategic
planning to ensure precise ball placement to target regions. The dynamic nature
of table tennis, coupled with its real-time response requirements, makes it
particularly well-suited for advancing robotic control capabilities in
fast-paced, precision-critical domains. In this paper, we present
SpikePingpong, a novel system that integrates spike-based vision with imitation
learning for high-precision robotic table tennis. Our approach introduces two
key attempts that directly address the aforementioned challenges: SONIC, a
spike camera-based module that achieves millimeter-level precision in
ball-racket contact prediction by compensating for real-world uncertainties
such as air resistance and friction; and IMPACT, a strategic planning module
that enables accurate ball placement to targeted table regions. The system
harnesses a 20 kHz spike camera for high-temporal resolution ball tracking,
combined with efficient neural network models for real-time trajectory
correction and stroke planning. Experimental results demonstrate that
SpikePingpong achieves a remarkable 91% success rate for 30 cm accuracy target
area and 71% in the more challenging 20 cm accuracy task, surpassing previous
state-of-the-art approaches by 38% and 37% respectively. These significant
performance improvements enable the robust implementation of sophisticated
tactical gameplay strategies, providing a new research perspective for robotic
control in high-speed dynamic tasks.
