— Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a...
Andrea Censi, Daniele Calisi, Alessandro De Luca, ...
Anytime algorithms, whose quality of results improves gradually as computation time increases, provide useful performance components for timecritical planning and control of robot...
- This paper presents a third-order spline interpolation based trajectory planning method which is aiming to achieve smooth biped swing leg trajectory by reducing the instant veloc...
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
—Consider the problem of a robot (learning mechanism or algorithm) attempting to locate a point on a line. The mechanism interacts with a random environment which essentially inf...