[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 …
Automated driving in uncertain environments: Planning with interaction and uncertain maneuver prediction
Automated driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …
uncertainty from the prediction originates from the noisy sensor data and from the fact that …
Rational quantitative attribution of beliefs, desires and percepts in human mentalizing
Social cognition depends on our capacity for 'mentalizing', or explaining an agent's
behaviour in terms of their mental states. The development and neural substrates of …
behaviour in terms of their mental states. The development and neural substrates of …
SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces
Motion planning in uncertain and dynamic environments is an essential capability for
autonomous robots. Partially observable Markov decision processes (POMDPs) provide a …
autonomous robots. Partially observable Markov decision processes (POMDPs) provide a …
Approximate information state for approximate planning and reinforcement learning in partially observed systems
We propose a theoretical framework for approximate planning and learning in partially
observed systems. Our framework is based on the fundamental notion of information state …
observed systems. Our framework is based on the fundamental notion of information state …
Planning under uncertainty for robotic tasks with mixed observability
Partially observable Markov decision processes (POMDPs) provide a principled, general
framework for robot motion planning in uncertain and dynamic environments. They have …
framework for robot motion planning in uncertain and dynamic environments. They have …
Collision avoidance for unmanned aircraft using Markov decision processes
Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance
systems must be developed. Instead of hand-crafting a collision avoidance algorithm for …
systems must be developed. Instead of hand-crafting a collision avoidance algorithm for …
Motion planning under uncertainty for robotic tasks with long time horizons
H Kurniawati, Y Du, D Hsu… - The International Journal …, 2011 - journals.sagepub.com
Motion planning with imperfect state information is a crucial capability for autonomous robots
to operate reliably in uncertain and dynamic environments. Partially observable Markov …
to operate reliably in uncertain and dynamic environments. Partially observable Markov …
POMDPs for robotic tasks with mixed observability
(POMDPs) provide a principled mathematical framework for motion planning of autonomous
robots in uncertain and dynamic environments. They have been successfully applied to …
robots in uncertain and dynamic environments. They have been successfully applied to …
Monte Carlo value iteration for continuous-state POMDPs
Partially observable Markov decision processes (POMDPs) have been successfully applied
to various robot motion planning tasks under uncertainty. However, most existing POMDP …
to various robot motion planning tasks under uncertainty. However, most existing POMDP …