ReSiReg: Towards Spatially Consistent Semantics in Language-Conditioned Robotic Tasks
arXiv:2606.19088v1 Announce Type: new Abstract: 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 languag
Overview
arXiv:2606.19088v1 Announce Type: new Abstract: 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
Source
Originally published at arxiv.org.
Related Articles
- Self-Supervised Mask-Aware Transformers for Fault-Tolerant FBG Force Sensing in Minimally Invasive Surgical Robotics
- EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
- A Scalable Embodied Intelligence Platform for Seamless Real-to-Sim-to-Real Transfer of Household Mobile Manipulation Tasks
Source: https://arxiv.org/abs/2606.19088
