Collaborative, Learning, and Adaptive Robots Lab at NUS

Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation

We introduce a novel framework centered around the Probable Object Location (POLo) score, which allows the agent to make data-driven decisions for efficient object search.

Embedding Symbolic Temporal Knowledge into Deep Sequential Models

We embed symbolic knowledge expressed as linear temporal logic (LTL) and use these embeddings to guide the training of deep sequential models.

Embedding Symbolic Knowledge into Deep Networks

Leveraging prior symbolic knowledge to improve the performance of deep models.