In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …

Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

Currently autonomous or self-driving vehicles are at the heart of academia and industry
research because of its multi-faceted advantages that includes improved safety, reduced …

Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

Decision-making for engineering systems management can be efficiently formulated using
Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs). Typical …

Autonomous robots facing a diversity of open environments and performing a variety of tasks
and interactions need explicit deliberation in order to fulfill their missions. Deliberation is …

This paper surveys recent results in pursuit-evasion and autonomous search relevant to
applications in mobile robotics. We provide a taxonomy of search problems that highlights …

We describe an integrated strategy for planning, perception, state estimation and action in
complex mobile manipulation domains based on planning in the belief space of probability …