Probabilistic Inference in Planning for Partially Observable Long Horizon Problems
Alphonsus Adu-Bredu,Nikhil Devraj,Pin-Han Lin,Zhen Zeng,Odest Chadwicke Jenkins,Alphonsus Adu-Bredu,Nikhil Devraj,Pin-Han Lin,Zhen Zeng,Odest Chadwicke Jenkins
For autonomous service robots to successfully perform long horizon tasks in the real world, they must act intelligently in partially observable environments. Most Task and Motion Planning approaches assume full observability of their state space, making them ineffective in stochastic and partially observable domains that reflect the uncertainties in the real world. We propose an online planning an...
