# Removing Infrastructure Barriers in Human-Robot Collaboration Through Wireless Reconfigurable Cells

arXiv: https://arxiv.org/abs/2608.09658 · HTML: https://mlsanity.com/paper/2608.09658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 11 of 11)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [11K Hands: Gender recognition and biometric identification using a large dataset of hand images](https://mlsanity.com/paper/1711.04322/) (2017)
- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images](https://mlsanity.com/paper/2103.02184/) (2021)
- [Object Detection Using Sim2Real Domain Randomization for Robotic Applications](https://mlsanity.com/paper/2208.04171/) (2022)
- [Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection?](https://mlsanity.com/paper/2312.02672/) (2023)
- [A Review of Prospects and Opportunities in Disassembly with Human-Robot Collaboration](https://mlsanity.com/paper/2310.13643/) (2023)
- [Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster](https://mlsanity.com/paper/2112.13194/) (2021)
- [Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications](https://mlsanity.com/paper/2211.01048/) (2022)
- [A Comprehensive Survey of Wireless Time-Sensitive Networking (TSN): Architecture, Technologies, Applications, and Open Issues](https://mlsanity.com/paper/2312.01204/) (2023)

## Abstract (arXiv)

Human-Robot Collaboration (HRC) plays a vital role in dynamic, high mix, low volume industrial scenarios such as remanufacturing, which frequently face workcell rearrangements. Traditional setups are constrained by power and data cabling, restricting modularity and reconfigurations, while the selection of commercial wireless devices suitable for real-time perception and safe collaboration are limited in availability. This paper presents a highly flexible, wireless, 5G-based system that serves as a versatile experimental testbed for applications including remanufacturing, operator training, and user studies. To eliminate infrastructure barriers, the workcell integrates a novel battery-powered, multi-sensor platform prototype. Additionally, to support operator safety and system adaptability across environmental shifts, the system integrates a computer vision module for object detection and pose estimation, further augmented for robust hand recognition. Trained on synthetic and real data, the model reliably detects oriented grasping poses and human hands across varying lighting and background conditions (with an mAP@50-95 of 97.74 +- 0.10% and a mean inference time of 12.5 ms). Offloading these computationally intensive tasks to the edge via 5G, the proposed architecture contributes to resolving the bandwidth-latency trade-off. To demonstrate portability, the system was implemented in both Hungary and Norway, and was evaluated across a combination of public and private, Standalone and Non-Standalone 5G infrastructures. The performed network experiments produced results in round-trip response times down to 12 ms in case of compatible network-device pairings, suitable for safe, adaptive HRC. However, these measurements also revealed practical limitations related to interoperability in current 5G deployments that should be addressed in future works.