4 papers
Finite-Time Analysis of MCTS in Continuous POMDP Planning
Da Kong, Vadim Indelman
This paper presents a finite-time analysis for Monte Carlo Tree Search (MCTS) in Partially Observable Markov Decision Processes (POMDPs), with probabilistic concentration bounds in…
Open-loop POMDP Simplification and Safe Skipping of Replanning with Formal Performance Guarantees
Da Kong, Vadim Indelman
Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical framework for decision-making under uncertainty. However, the exact solution to POMDPs is…
Keyframe-Based Feed-Forward Visual Odometry
Weichen Dai, Wenhan Su, Da Kong +2
The emergence of visual foundation models has revolutionized visual odometry~(VO) and SLAM, enabling pose estimation and dense reconstruction within a single feed-forward network.…
Simplified POMDP Planning with an Alternative Observation Space and Formal Performance Guarantees
Da Kong, Vadim Indelman
Online planning under uncertainty in partially observable domains is an essential capability in robotics and AI. The partially observable Markov decision process (POMDP) is a mathe…