[HTML][HTML] Partially observable markov decision processes and robotics
H Kurniawati - Annual Review of Control, Robotics, and …, 2022 - annualreviews.org
Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …
Precision medicine
MR Kosorok, EB Laber - Annual review of statistics and its …, 2019 - annualreviews.org
Precision medicine seeks to maximize the quality of health care by individualizing the health-
care process to the uniquely evolving health status of each patient. This endeavor spans a …
care process to the uniquely evolving health status of each patient. This endeavor spans a …
Partially observable markov decision processes in robotics: A survey
Noisy sensing, imperfect control, and environment changes are defining characteristics of
many real-world robot tasks. The partially observable Markov decision process (POMDP) …
many real-world robot tasks. The partially observable Markov decision process (POMDP) …
A probabilistic graphical model foundation for enabling predictive digital twins at scale
MG Kapteyn, JVR Pretorius, KE Willcox - Nature Computational …, 2021 - nature.com
A unifying mathematical formulation is needed to move from one-off digital twins built
through custom implementations to robust digital twin implementations at scale. This work …
through custom implementations to robust digital twin implementations at scale. This work …
Deep reinforcement learning for autonomous internet of things: Model, applications and challenges
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …
around the world, where the IoT devices collect and share information to reflect status of the …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
A unified framework for stochastic optimization
WB Powell - European Journal of Operational Research, 2019 - Elsevier
Stochastic optimization is an umbrella term that includes over a dozen fragmented
communities, using a patchwork of sometimes overlapping notational systems with …
communities, using a patchwork of sometimes overlapping notational systems with …
Memory-based control with recurrent neural networks
Partially observed control problems are a challenging aspect of reinforcement learning. We
extend two related, model-free algorithms for continuous control--deterministic policy …
extend two related, model-free algorithms for continuous control--deterministic policy …
Olfactory sensing and navigation in turbulent environments
Fluid turbulence is a double-edged sword for the navigation of macroscopic animals, such
as birds, insects, and rodents. On the one hand, turbulence enables pheromone …
as birds, insects, and rodents. On the one hand, turbulence enables pheromone …
Interactive perception: Leveraging action in perception and perception in action
Recent approaches in robot perception follow the insight that perception is facilitated by
interaction with the environment. These approaches are subsumed under the term …
interaction with the environment. These approaches are subsumed under the term …