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/.

ReSiReg: Towards Spatially Consistent Semantics in Language-Conditioned Robotic Tasks

Vision-Language Models (VLMs) enable robots to follow open-language instructions. However, dense VLM embeddings have shown to be noisy and lack spatial consistency. This is problematic for robotic applications, which require simultaneous reasoning over semantics and 3D space. We examine spatial structure across recent VLMs and propose ReSiReg, a feature reconstruction method that uses spatially consistent VLM intermediates to improve dense language-grounded retrieval. ReSiReg clusters intermediates into visual prototypes, derives their language descriptors, and reconstructs each patch as a soft mixture of prototype-level language embeddings. We evaluate quantitatively on OVSS and 3D mapping across backbones, and qualitatively in real-world manipulation scenes. Quantitative results show improved dense retrieval; manipulation scenes show more spatially consistent target activations. We further provide a compact 25M dense VLM for robotic applications, substantially smaller than and competitive with ViT-B baselines. Available at https://resireg.github.io

From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models

Robots are increasingly envisioned to interact in real-world scenarios, where they must continuously adapt to new situations. To detect and grasp novel objects, zero-shot pose estimators determine poses without prior knowledge. Recently, vision language models (VLMs) have shown considerable advances in robotics applications by establishing an understanding between language input and image input. In our work, we take advantage of VLMs zero-shot capabilities and translate this ability to 6D object pose estimation. We propose a novel framework for promptable zero-shot 6D object pose estimation using language embeddings. The idea is to derive a coarse location of an object based on the relevancy map of a language-embedded NeRF reconstruction and to compute the pose estimate with a point cloud registration method. Additionally, we provide an analysis of LERF's suitability for open-set object pose estimation. We examine hyperparameters, such as activation thresholds for relevancy maps and investigate the zero-shot capabilities on an instance- and category-level. Furthermore, we plan to conduct robotic grasping experiments in a real-world setting.

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.