# DiG-Net: Enhancing Human-Robot Interaction through Hyper-Range Dynamic Gesture Recognition in Assistive Robotics

arXiv: https://arxiv.org/abs/2505.24786 · HTML: https://mlsanity.com/paper/2505.24786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Continuous Sign Language Recognition with Correlation Network](https://mlsanity.com/paper/2303.03202/) (2023)
- [Robotic Vision for Human-Robot Interaction and Collaboration: A Survey and Systematic Review](https://mlsanity.com/paper/2307.15363/) (2023)
- [Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspective](https://mlsanity.com/paper/2304.11293/) (2023)
- [Dynamic Gesture Recognition by Using CNNs and Star RGB: a Temporal Information Condensation](https://mlsanity.com/paper/1904.08505/) (2019)
- [Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction](https://mlsanity.com/paper/2311.15361/) (2023)
- [High-density Electromyography for Effective Gesture-based Control of Physically Assistive Mobile Manipulators](https://mlsanity.com/paper/2312.07745/) (2023)
- [Real-time Human Finger Pointing Recognition and Estimation for Robot Directives Using a Single Web-Camera](https://mlsanity.com/paper/2307.02949/) (2023)
- [Hand and Arm Gesture-based Human-Robot Interaction: A Review](https://mlsanity.com/paper/2209.08229/) (2022)
- [Robust and customized methods for real-time hand gesture recognition under object-occlusion](https://mlsanity.com/paper/1809.05911/) (2018)

## Abstract (arXiv)

Dynamic hand gestures play a pivotal role in assistive human-robot interaction (HRI), facilitating intuitive, non-verbal communication, particularly for individuals with mobility constraints or those operating robots remotely. Current gesture recognition methods are mostly limited to short-range interactions, reducing their utility in scenarios demanding robust assistive communication from afar. In this paper, we present DiG-Net, the first dynamic gesture recognition framework enabling robust operation at hyper-range distances of up to 30 meters, specifically designed for assistive robotics to enhance accessibility and improve quality of life. Our proposed Distance-aware Gesture Network (DiG-Net) effectively combines Depth-Conditioned Deformable Alignment (DADA) blocks with Spatio-Temporal Graph modules, enabling robust processing and classification of gesture sequences captured under challenging conditions, including significant physical attenuation, reduced resolution, and dynamic gesture variations commonly experienced in real-world assistive environments. We further introduce the Radiometric Spatio-Temporal Depth Attenuation Loss (RSTDAL), shown to enhance learning and strengthen model robustness across varying distances. Our model demonstrates significant performance improvement over state-of-the-art gesture recognition frameworks, achieving a recognition accuracy of 97.3% on a diverse dataset with challenging hyper-range gestures. By effectively interpreting gestures from considerable distances, DiG-Net significantly enhances the usability of assistive robots in home healthcare, industrial safety, and remote assistance scenarios, enabling seamless and intuitive interactions for users regardless of physical limitations.