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We propose an assume-guarantee reasoning (AGR) framework for verification problem of a system with two components modeled by Markov Decision Process (MDP) and Partially Observable MDP (POMDP), respectively. MDP-POMDP model describes system's sensing, actuation and environment uncertainties, which can be used in the modeling of systems containing different subsystems, e.g., human-robot collaboration...
Partially Observable Markov Decision Process (POMDP) has been widely used in the robotics to model uncertainties from sensors, actuators and the environment. However, such comprehensiveness makes the planning in POMDP generally very difficult. Existing work often searches for an optimal control policy with respect to predefined reward functions, which may require a large memory and is computationally...
Formal methods in robotic motion planning have emerged as a hot research topic recently due to its correct-by-design nature, and most results haven been based on nonprobabilistic discrete models. To better handle the environment uncertainties, sensor noise and actuator imperfection, control problems in probabilistic systems like Markov Chain (MC) and Markov Decision Process (MDP) have also been studied...
The covariance region descriptor recently proposed in [1] has been proved robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized. The similarity of two covariance descriptor is measured on Riemannian manifolds. Relying on the same...
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