Evaluating Pointing Gestures for Target Selection in Human-Robot Collaboration
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916812997787648 |
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| author | Sassali, Noora Pieters, Roel |
| author_facet | Sassali, Noora Pieters, Roel |
| contents | Pointing gestures are a common interaction method used in Human-Robot Collaboration for various tasks, ranging from selecting targets to guiding industrial processes. This study introduces a method for localizing pointed targets within a planar workspace. The approach employs pose estimation, and a simple geometric model based on shoulder-wrist extension to extract gesturing data from an RGB-D stream. The study proposes a rigorous methodology and comprehensive analysis for evaluating pointing gestures and target selection in typical robotic tasks. In addition to evaluating tool accuracy, the tool is integrated into a proof-of-concept robotic system, which includes object detection, speech transcription, and speech synthesis to demonstrate the integration of multiple modalities in a collaborative application. Finally, a discussion over tool limitations and performance is provided to understand its role in multimodal robotic systems. All developments are available at: https://github.com/NMKsas/gesture_pointer.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Pointing Gestures for Target Selection in Human-Robot Collaboration Sassali, Noora Pieters, Roel Robotics Computer Vision and Pattern Recognition Pointing gestures are a common interaction method used in Human-Robot Collaboration for various tasks, ranging from selecting targets to guiding industrial processes. This study introduces a method for localizing pointed targets within a planar workspace. The approach employs pose estimation, and a simple geometric model based on shoulder-wrist extension to extract gesturing data from an RGB-D stream. The study proposes a rigorous methodology and comprehensive analysis for evaluating pointing gestures and target selection in typical robotic tasks. In addition to evaluating tool accuracy, the tool is integrated into a proof-of-concept robotic system, which includes object detection, speech transcription, and speech synthesis to demonstrate the integration of multiple modalities in a collaborative application. Finally, a discussion over tool limitations and performance is provided to understand its role in multimodal robotic systems. All developments are available at: https://github.com/NMKsas/gesture_pointer.git. |
| title | Evaluating Pointing Gestures for Target Selection in Human-Robot Collaboration |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.22116 |