4 accepted papers
Online planning for partially observable Markov decision processes (POMDPs) provides efficient techniques for robot decision-making under uncertainty. However, existing methods fall short of preventing safety violations in dynamic environments. This letter presents a novel safe POMDP online planning
Partially observable Markov decision processes (POMDPs) have been widely used in many robotic applications for sequential decision-making under uncertainty. POMDP online planning algorithms such as Partially Observable Monte-Carlo Planning (POMCP) can solve very large POMDPs with the goal of maximiz…
Recent work has considered trust-aware decision making for human-robot collaboration (HRC) with a focus on model learning. In this paper, we are interested in enabling the HRC system to complete complex tasks specified using temporal logic formulas that involve human trust. Since accurately observin…
Brain-computer interface (BCI) systems can translate the human mind into control commands, which makes it feasible to improve the life quality of physically challenged people. However, in real-life situations, it is still difficult for users to utilize robots to provide basic services with BCI syste…