ART-VS: Adaptive Resolution Tiling for Vision Transformer Visual Servoing

Visual servoing with self-supervised Vision Transformer (ViT) features enables training-free robotic positioning with strong generalization, but faces a fundamental trade-off between robustness and precision. Coarse patch-level descriptors provide stable correspondences yet limit positioning accuracy. Increasing image resolution improves precision but yields only marginal robustness gains - under perturbation, high-resolution processing improves convergence success rate from 76.6% to just 81.0% despite 12x more ViT patches. Therefore, we propose Adaptive Resolution Tiling Visual Servoing (ART-VS), a two-phase method that adapts feature granularity to servoing progress: a coarse phase at native ViT resolution for stable alignment, then a tiled high-resolution phase that restricts matching to local neighborhoods improving positioning accuracy. Without any task-specific training, ART-VS achieves 95.4% convergence under perturbation, outperforming standard and full-resolution ViT-based servoing by 18.8 and 14.4 percentage points. Over the former it reduces positioning error by 53%, while running at over 10x higher speed and 27% lower VRAM than the latter. We validate ART-VS across three ViT backbones and demonstrate real-world category-level grasping of unseen object instances, achieving 95/100 on transparent bottles and 98/100 on shoes. Code available under https://art-vs.github.io/.

Semi-Autonomous Mobile Search and Rescue Robot for Radiation Disaster Scenarios

This paper describes a novel semi-autonomous mobile robot system designed to assist search and rescue (SAR) first responders in disaster scenarios. While robots offer significant potential in SAR missions, current solutions are limited in their ability to handle a diverse range of tasks. This gap is addressed by presenting a system capable of (1) autonomous navigation and mapping, allowing the robot to autonomously explore and map areas affected by catastrophic events, (2) radiation mapping, enabling the system to triangulate a radiation map from discrete radiation measurements to aid in identifying hazardous areas, (3) semi-autonomous substance sampling, allowing the robot to collect samples of suspicious substances and analyze them onboard with immediate classification, and (4) valve manipulation, enabling teleoperated closing of valves that control hazardous material flow. This semi-autonomous approach balances human control over critical tasks like substance sampling with efficient robot navigation in low-risk areas. The system is evaluated during three trials that simulate possible disaster scenarios, two of which have been recorded during the European Robotics Hackathon (EnRicH). Furthermore, we provide recorded sensor data as well as the implemented software system as supplemental material through a GitHub repository: https://github.com/TW-Robotics/search-and-rescue-robot-IROS2024.