arXiv：cs.AI· Mil\'an Zsolt Bagladi, L\'aszl\'o Guly\'as·· 3 小时前
神经网络如何实现时序模式识别与动态手臂手势速度估计以控制机器人
Neural Networks for Temporal Pattern Recognition and Dynamic Arm Gesture Speed Estimation for Robot Control
AI 导读
研究对18种神经网络架构在10个序列任务上系统基准测试，BiGRU、TCN、Conv1D 和 GRUReLU 四款架构表现最佳，参数量均低于2,000。基于该排名，研究者用 BiGRU、TCN 和 GRUReLU 从骨骼关键点序列估计动态手臂手势速度，在含256,710帧、8类交通手势的自建数据集上，峰值计数解释的平均绝对误差为0.198（约5%相对误差），周期时间解释约4%，平均尖峰间距约8%。
正文
Abstract:Deploying intelligent robotic systems that interact with humans through gestures requires neural networks capable of recognizing diverse temporal patterns. We present a systematic benchmark of ten abstract sequential tasks--five permutation-invariant (set) and five order-dependent (sequence) problems--evaluated across eighteen neural network architectures spanning recurrent, convolutional, attention-based, and set-function families. Beyond the core architecture-task grid, we explore numerous preprocessing and target-variable transformations, yielding more than 250 distinct experimental configurations. All variants are trained and tested under strictly identical conditions (fixed random seeds, shared hyperparameters, shared data splits) to ensure fair and reproducible comparison. Ranking across all ten tasks reveals four consistently top-performing architectures--BiGRU, TCN, Conv1D, and GRUReLU--all compact enough for real-time deployment (under 2,000 parameters in the benchmark setting). Based on this ranking, we apply three architecturally diverse top models (BiGRU, TCN, and GRUReLU) to a practical robotics problem: estimating the execution speed of dynamic arm gestures from skeletal keypoint sequences. Three speed interpretations (peak count, period time, and mean spike spacing) are evaluated on a custom dataset of eight traffic-related gesture classes comprising 256,710 frames recorded via OpenPose. The best configuration achieves a mean absolute error of 0.198 on the peak-count interpretation, corresponding to roughly 5% relative error, while the period-time interpretation reaches approximately 4% relative error, and the mean spike spacing interpretation approximately 8% relative error. These results demonstrate that neural networks can reliably estimate gesture speed from skeletal data, opening a path toward speed-aware gesture-controlled robotic systems.
| Comments: | 10 pages, 6 figures, 4 tables. Published in Proceedings of the Intelligent Robotics FAIR 2026 (IntRob '26), June 18-19, 2026, Budapest, Hungary, ACM |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.11631 [cs.RO] |
| (or arXiv:2610.11631v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11631 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Proceedings of the Intelligent Robotics FAIR 2026 (IntRob '26), ACM, 2026, pp. 140-149 |
| Related DOI: | https://doi.org/10.1145/3831600.3831618
DOI(s) linking to related resources |
Submission history
From: Milán Zsolt Bagladi [view email]
[v1]
Thu, 8 Oct 2026 10:08:56 UTC (1,403 KB)
来源：arXiv：cs.AI · arxiv.org