Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces a training-free semantic navigation pipeline that leverages a compact room lexicon to mediate object relationships for zero-shot ObjectNav tasks. By mapping object labels to CLIP-derived Room Probability Vectors (RPVs) and utilizing geodesic flood-fill propagation for scoring, the authors demonstrate improved navigation performance on the HM3D dataset. The proposed method enhances Success Rate and Success by weighted inverse Path Length by 3% and 1.3%, respectively, while maintaining interpretability and flexibility in open-vocabulary settings.
Object-centric navigation can outperform traditional image-holistic approaches by leveraging spatial co-occurrence without the need for extensive training.
Zero-shot ObjectNav methods increasingly use vision-language priors, but direct object-object similarity in the latent space is often a weak proxy for spatial co-occurrence. We present an analytical, training-free semantic navigation pipeline that mediates object relationships through a compact room lexicon. Each object label is mapped to a CLIP-derived Room Probability Vector (RPV), and object-target co-occurrence is computed from RPV distribution overlap. These scores are projected onto a value map using geodesic flood-fill propagation (Fast Marching Method), with adaptive signal decay, and are used to rank frontiers by semantic score for navigation. Together, these components form an integrated, training-free, object-centric pipeline for open-vocabulary zero-shot navigation. Results show that our object-centric approach improves Success Rate (SR) and Success by weighted inverse Path Length (SPL) by a relative 3% and 1.3%, respectively, compared to image-holistic baselines on the HM3D dataset validation split, while preserving interpretability and open-vocabulary flexibility. Code is available at: uts-ri.github.io/RPV-SemNav.