The Neural Compass: Probabilistic Relative Feature Fields for Robotic Search
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 ...
| Authors | Gabriele Somaschini, Adrian Röfer, Abhinav Valada |
|---|---|
| Primary category | cs.RO |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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