OpenNovelty.org

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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
RoboticsImitation Learning
Abstract:

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 \textit{\textbf{SpikePingpong}}, a novel system that integrates spike-based vision with imitation learning for high-precision robotic table tennis. We develop a cognitive-inspired 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 \textit{\textbf{SpikePingpong}} 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 cognitive-inspired architectures for advancing robotic capabilities in time-critical manipulation tasks.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces SpikePingpong, a system combining spike-based neuromorphic vision with imitation learning for robotic table tennis. It resides in the High-Speed and Spike-Based Vision leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader taxonomy of eighteen papers. This leaf focuses specifically on neuromorphic or high-frequency sensing for rapid ball tracking, distinguishing it from conventional frame-based approaches that dominate neighboring categories like Low-Cost Vision Approaches and Multimodal Sensor Fusion.

The taxonomy reveals that vision-centric work clusters into three distinct sensor philosophies: high-speed/spike-based systems prioritizing temporal resolution, low-cost single-camera setups emphasizing accessibility, and multimodal fusion architectures combining complementary sensors. SpikePingpong's Fast-Slow architecture draws conceptual inspiration from dual-system cognitive theory, positioning it at the intersection of perception and control. Neighboring leaves in Learning and Control Strategies include reinforcement learning methods and dynamic trajectory generation, yet the paper's imitation-based approach aligns more closely with Imitation and Demonstration-Based Learning, suggesting cross-branch integration of vision innovation with established learning paradigms.

Among twenty-five candidates examined, none clearly refute the three core contributions. The Fast-Slow system architecture examined ten candidates with zero refutations, the neural error correction framework examined five with none overlapping, and the IMPACT control method examined ten with no prior work providing equivalent functionality. This limited search scope—focused on top-K semantic matches—suggests the specific combination of spike vision, dual-system perception, and imitation-based control has not been extensively documented in the accessible literature, though individual components like spike-based sensing or imitation learning appear separately in related work.

The analysis reflects a constrained literature snapshot rather than exhaustive coverage. While the sparse High-Speed and Spike-Based Vision leaf and absence of refutations among examined candidates suggest novelty in the integrated approach, the small taxonomy size and limited candidate pool mean potentially relevant work outside the top-25 semantic matches remains unexamined. The contribution appears to occupy a niche intersection of neuromorphic sensing and learned control that existing surveys have not densely populated.

Taxonomy

Core-task Taxonomy Papers
18
3
Claimed Contributions
25
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: robotic table tennis with high-speed vision and imitation learning. The field organizes around three main branches that reflect distinct technical challenges. Vision Systems and Ball Tracking encompasses methods for perceiving fast-moving balls, ranging from conventional high-speed cameras to emerging spike-based sensors that promise lower latency and power consumption. Learning and Control Strategies addresses how robots acquire skills—whether through imitation, reinforcement learning, or hybrid approaches—and how they execute precise, dynamic strokes under tight timing constraints. Mobile and Bimanual Manipulation Systems explores platforms that combine locomotion with dexterous manipulation, exemplified by works like Mobile ALOHA[3], extending table tennis capabilities beyond fixed-base arms to whole-body coordination. Together, these branches capture the interplay between perception speed, control fidelity, and physical versatility that defines robotic table tennis research. Recent efforts reveal contrasting philosophies in sensor design and learning paradigms. Traditional high-speed vision systems, such as those surveyed in High Speed Vision[4], deliver rich frame-based data but at the cost of bandwidth and processing overhead, while newer spike-based approaches aim for event-driven efficiency. On the learning side, some studies emphasize end-to-end imitation from human demonstrations, whereas others blend model-based trajectory planning with data-driven refinement to handle the sport's combinatorial stroke variations. SpikePingpong[0] sits squarely within the high-speed and spike-based vision cluster, leveraging neuromorphic sensors to achieve ultra-low-latency ball tracking paired with imitation learning for stroke generation. This positions it close to SpikePingpong Learning[11], which shares the spike-based sensing theme, yet SpikePingpong[0] places stronger emphasis on integrating the vision pipeline directly with learned control policies. Compared to frame-based methods like Ping Pong Tracking[2], the spike approach trades off image completeness for temporal precision, reflecting an ongoing debate about the optimal sensor modality for dynamic interception tasks.

Claimed Contributions

SpikePingpong: Fast-Slow system architecture for robotic table tennis

The authors present SpikePingpong, a novel robotic table tennis system that integrates spike-based vision with a dual-system architecture inspired by cognitive theory. System 1 provides rapid ball detection and physics-based trajectory prediction, while System 2 employs spike-oriented neural calibration for precise hittable position corrections.

10 retrieved papers
Fast-Slow perception framework with neural error correction

The authors develop a perception framework where System 1 uses RGB-D cameras for rapid detection and System 2 leverages high-frequency spike camera data to learn systematic deviations between physics-based predictions and actual optimal interception positions, compensating for real-world effects like air resistance and ball spin.

5 retrieved papers
IMPACT: Imitation-based Motion Planning And Control Technology

The authors introduce IMPACT, a module that learns strategic ball striking through imitation learning by mapping incoming trajectory characteristics to optimal robotic arm striking policies. This enables the robot to execute tactical returns to specified target regions rather than merely intercepting the ball.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

SpikePingpong: Fast-Slow system architecture for robotic table tennis

The authors present SpikePingpong, a novel robotic table tennis system that integrates spike-based vision with a dual-system architecture inspired by cognitive theory. System 1 provides rapid ball detection and physics-based trajectory prediction, while System 2 employs spike-oriented neural calibration for precise hittable position corrections.

Contribution

Fast-Slow perception framework with neural error correction

The authors develop a perception framework where System 1 uses RGB-D cameras for rapid detection and System 2 leverages high-frequency spike camera data to learn systematic deviations between physics-based predictions and actual optimal interception positions, compensating for real-world effects like air resistance and ball spin.

Contribution

IMPACT: Imitation-based Motion Planning And Control Technology

The authors introduce IMPACT, a module that learns strategic ball striking through imitation learning by mapping incoming trajectory characteristics to optimal robotic arm striking policies. This enables the robot to execute tactical returns to specified target regions rather than merely intercepting the ball.

Table of Contents