The Neural Compass: Probabilistic Relative Feature Fields for Robotic Search

2026-03-09

AI Summary

This work addresses the challenge of improving object search efficiency for robots in unfamiliar environments by implicitly learning object co-occurrence relationships from unlabeled observational data. The authors propose the Probabilistic Relative Feature Field (ProReFF) model, which leverages a pre-trained vision-language model to extract semantic features and constructs semantic priors by predicting the relative distribution among these features. A learning-driven alignment mechanism is introduced to integrate inconsistent unlabeled observations into a coherent probabilistic distribution, guiding the agent toward regions with higher likelihood of containing the target object. To the best of the authors’ knowledge, this is the first approach to implicitly model semantic spatial relationships between objects solely from unlabeled observations. Evaluated on 100 challenging scenes from Matterport3D, the method improves search efficiency by 20% over the strongest baseline and achieves 80% of human-level performance.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Object co-occurrences provide a key cue for finding objects successfully and efficiently in unfamiliar environments. Typically, one looks for cups in kitchens and views fridges as evidence of being in a kitchen. Such priors have also been exploited in artificial agents, but they are typically learned from explicitly labeled data or queried from language models. It is still unclear whether these relations can be learned implicitly from unlabeled observations alone. In this work, we address this problem and propose ProReFF, a feature field model trained to predict relative distributions of features obtained from pre-trained vision language models. In addition, we introduce a learning-based strategy that enables training from unlabeled and potentially contradictory data by aligning inconsistent observations into a coherent relative distribution. For the downstream object search task, we propose an agent that leverages predicted feature distributions as a semantic prior to guide exploration toward regions with a high likelihood of containing the object. We present extensive evaluations demonstrating that ProReFF captures meaningful relative feature distributions in natural scenes and provides insight into the impact of our proposed alignment step. We further evaluate the performance of our search agent in 100 challenges in the Matterport3D simulator, comparing with feature-based baselines and human participants. The proposed agent is 20% more efficient than the strongest baseline and achieves up to 80% of human performance.

Problem

• object co-occurrence
• unlabeled data
• semantic prior
• robotic search
• relative feature distribution

Innovation

• Probabilistic Relative Feature Fields
• Unsupervised Semantic Priors
• Object Co-occurrence Learning
• Vision-Language Features
• Robotic Object Search