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» Parametric POMDPs for planning in continuous state spaces
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ICML
1999
IEEE
15 years 10 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
AUTOMATICA
2007
82views more  AUTOMATICA 2007»
14 years 10 months ago
Simulation-based optimal sensor scheduling with application to observer trajectory planning
The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous....
Sumeetpal S. Singh, Nikolaos Kantas, Ba-Ngu Vo, Ar...
ICRA
2010
IEEE
136views Robotics» more  ICRA 2010»
14 years 7 months ago
Efficient planning under uncertainty for a target-tracking micro-aerial vehicle
A helicopter agent has to plan trajectories to track multiple ground targets from the air. The agent has partial information of each target's pose, and must reason about its u...
Ruijie He, Abraham Bachrach, Nicholas Roy
PUK
2000
14 years 11 months ago
Knowledge-Based Control of Decision Theoretic Planning - Adaptive Planning Model Selection
This paper proposes a new planning architecture for agents operating in uncertain and dynamic environments. Decisiontheoretic planning has been recognized as a useful tool for rea...
Jun Miura, Yoshiaki Shirai
CAV
2007
Springer
145views Hardware» more  CAV 2007»
15 years 1 months ago
Hybrid Systems: From Verification to Falsification
We propose HyDICE, Hybrid DIscrete Continuous Exploration, a multi-layered approach for hybrid-system testing that integrates continuous sampling-based robot motion planning with d...
Erion Plaku, Lydia E. Kavraki, Moshe Y. Vardi