16,156 research outputs found
Belief Tree Search for Active Object Recognition
Active Object Recognition (AOR) has been approached as an unsupervised
learning problem, in which optimal trajectories for object inspection are not
known and are to be discovered by reducing label uncertainty measures or
training with reinforcement learning. Such approaches have no guarantees of the
quality of their solution. In this paper, we treat AOR as a Partially
Observable Markov Decision Process (POMDP) and find near-optimal policies on
training data using Belief Tree Search (BTS) on the corresponding belief Markov
Decision Process (MDP). AOR then reduces to the problem of knowledge transfer
from near-optimal policies on training set to the test set. We train a Long
Short Term Memory (LSTM) network to predict the best next action on the
training set rollouts. We sho that the proposed AOR method generalizes well to
novel views of familiar objects and also to novel objects. We compare this
supervised scheme against guided policy search, and find that the LSTM network
reaches higher recognition accuracy compared to the guided policy method. We
further look into optimizing the observation function to increase the total
collected reward of optimal policy. In AOR, the observation function is known
only approximately. We propose a gradient-based method update to this
approximate observation function to increase the total reward of any policy. We
show that by optimizing the observation function and retraining the supervised
LSTM network, the AOR performance on the test set improves significantly.Comment: IROS 201
Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes
Civil and maritime engineering systems, among others, from bridges to
offshore platforms and wind turbines, must be efficiently managed as they are
exposed to deterioration mechanisms throughout their operational life, such as
fatigue or corrosion. Identifying optimal inspection and maintenance policies
demands the solution of a complex sequential decision-making problem under
uncertainty, with the main objective of efficiently controlling the risk
associated with structural failures. Addressing this complexity, risk-based
inspection planning methodologies, supported often by dynamic Bayesian
networks, evaluate a set of pre-defined heuristic decision rules to reasonably
simplify the decision problem. However, the resulting policies may be
compromised by the limited space considered in the definition of the decision
rules. Avoiding this limitation, Partially Observable Markov Decision Processes
(POMDPs) provide a principled mathematical methodology for stochastic optimal
control under uncertain action outcomes and observations, in which the optimal
actions are prescribed as a function of the entire, dynamically updated, state
probability distribution. In this paper, we combine dynamic Bayesian networks
with POMDPs in a joint framework for optimal inspection and maintenance
planning, and we provide the formulation for developing both infinite and
finite horizon POMDPs in a structural reliability context. The proposed
methodology is implemented and tested for the case of a structural component
subject to fatigue deterioration, demonstrating the capability of
state-of-the-art point-based POMDP solvers for solving the underlying planning
optimization problem. Within the numerical experiments, POMDP and
heuristic-based policies are thoroughly compared, and results showcase that
POMDPs achieve substantially lower costs as compared to their counterparts,
even for traditional problem settings
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