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Neural networks benchmarked for robot gesture control speed estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have benchmarked eighteen neural network architectures on ten abstract sequential tasks to identify effective models for temporal pattern recognition. The study found that BiGRU, TCN, Conv1D, and GRUReLU consistently performed best, all with parameters suitable for real-time deployment. These top models were then applied to a robotics problem, specifically estimating the speed of dynamic arm gestures from skeletal keypoint sequences using a custom dataset. The best configurations achieved low relative errors in estimating gesture speed, demonstrating the potential for speed-aware gesture-controlled robotic systems. AI

IMPACT Demonstrates neural networks can reliably estimate gesture speed from skeletal data, enabling speed-aware gesture-controlled robotic systems.

RANK_REASON Academic paper detailing a benchmark of neural network architectures for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Neural networks benchmarked for robot gesture control speed estimation

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Academic paper detailing a benchmark of neural network architectures for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mil\'an Zsolt Bagladi, L\'aszl\'o Guly\'as ·

    Neural Networks for Temporal Pattern Recognition and Dynamic Arm Gesture Speed Estimation for Robot Control

    arXiv:2610.11631v1 Announce Type: cross 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 p…

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