LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based Planning
We develop a safe planning method for trajectory generation by sampling from diffusion model under different LTLf constraints.
Videos
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.