用于时间模式识别与机器人控制的动态手臂手势速度估计的神经网络
Neural Networks for Temporal Pattern Recognition and Dynamic Arm Gesture Speed Estimation for Robot Control
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中文总结 AI 辅助
该研究通过系统性基准测试筛选出4种适用于时间模式识别的高效神经网络，将其中3种应用于机器人动态手臂手势速度估计，在自定义数据集上取得了约4%-8%的相对误差，为速度感知的手势控制机器人提供了可行方案。
中文摘要 AI 辅助
部署通过手势与人类交互的智能机器人系统，需要能够识别多样时间模式的神经网络。我们对10项抽象序列任务开展系统性基准测试，其中5项为排列不变（集合）问题、5项为依赖顺序（序列）问题，测试覆盖18种神经网络架构，涵盖循环、卷积、注意力机制及集合函数类。除核心架构-任务网格外，我们还探索了大量预处理与目标变量变换，得到250余种不同实验配置。所有变体均在严格相同条件（固定随机种子、共享超参数、共享数据划分）下训练与测试，以确保公平且可复现的比较。对10项任务的排名显示，4种始终表现最优的架构为BiGRU、TCN、Conv1D与GRUReLU，在基准设置下参数均少于2000，足以支持实时部署。基于该排名，我们将3种架构各异的最优模型（BiGRU、TCN、GRUReLU）应用于实际机器人问题：从骨骼关键点序列估计动态手臂手势的执行速度。我们在自定义数据集上评估3种速度解释方式（峰值计数、周期时间、平均峰值间距），该数据集包含通过OpenPose记录的8类交通相关手势，共256710帧。最优配置在峰值计数解释上的平均绝对误差为0.198，对应约5%的相对误差；周期时间解释的相对误差约为4%；平均峰值间距解释的相对误差约为8%。这些结果表明，神经网络可从骨骼数据可靠估计手势速度，为实现感知速度的手势控制机器人系统开辟了路径。
英文摘要
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.
发表机构
- ELTE Eötvös Loránd University（厄特沃什·罗兰大学）
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