SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation
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arXiv
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| Format: | Preprint |
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2025
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| author | Vinod, Krishna Ramesh, Prithvi Jai N, Pavan Kumar B Chakravarthi, Bharatesh |
| author_facet | Vinod, Krishna Ramesh, Prithvi Jai N, Pavan Kumar B Chakravarthi, Bharatesh |
| contents | Event cameras offer microsecond latency, high dynamic range, and low power consumption, making them ideal for real-time robotic perception under challenging conditions such as motion blur, occlusion, and illumination changes. However, despite their advantages, synthetic event-based vision remains largely unexplored in mainstream robotics simulators. This lack of simulation setup hinders the evaluation of event-driven approaches for robotic manipulation and navigation tasks. This work presents an open-source, user-friendly v2e robotics operating system (ROS) package for Gazebo simulation that enables seamless event stream generation from RGB camera feeds. The package is used to investigate event-based robotic policies (ERP) for real-time navigation and manipulation. Two representative scenarios are evaluated: (1) object following with a mobile robot and (2) object detection and grasping with a robotic manipulator. Transformer-based ERPs are trained by behavior cloning and compared to RGB-based counterparts under various operating conditions. Experimental results show that event-guided policies consistently deliver competitive advantages. The results highlight the potential of event-driven perception to improve real-time robotic navigation and manipulation, providing a foundation for broader integration of event cameras into robotic policy learning. The GitHub repo for the dataset and code: https://eventbasedvision.github.io/SEBVS/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17643 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation Vinod, Krishna Ramesh, Prithvi Jai N, Pavan Kumar B Chakravarthi, Bharatesh Robotics Computer Vision and Pattern Recognition Event cameras offer microsecond latency, high dynamic range, and low power consumption, making them ideal for real-time robotic perception under challenging conditions such as motion blur, occlusion, and illumination changes. However, despite their advantages, synthetic event-based vision remains largely unexplored in mainstream robotics simulators. This lack of simulation setup hinders the evaluation of event-driven approaches for robotic manipulation and navigation tasks. This work presents an open-source, user-friendly v2e robotics operating system (ROS) package for Gazebo simulation that enables seamless event stream generation from RGB camera feeds. The package is used to investigate event-based robotic policies (ERP) for real-time navigation and manipulation. Two representative scenarios are evaluated: (1) object following with a mobile robot and (2) object detection and grasping with a robotic manipulator. Transformer-based ERPs are trained by behavior cloning and compared to RGB-based counterparts under various operating conditions. Experimental results show that event-guided policies consistently deliver competitive advantages. The results highlight the potential of event-driven perception to improve real-time robotic navigation and manipulation, providing a foundation for broader integration of event cameras into robotic policy learning. The GitHub repo for the dataset and code: https://eventbasedvision.github.io/SEBVS/ |
| title | SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.17643 |