Q-learning, a most widely used reinforcement learning method, normally needs well-defined quantized state and action spaces to converge. This makes it difficult to be applied to re...
Abstract— Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques w...
— This paper proposes an optimal gait generation framework using virtual constraint and learning optimal control. In this method, firstly, we add a constraint by a virtual poten...
— A key problem in deploying sensor networks in real-world applications is that of mapping, i.e. determining the location of each sensor such that subsequent tasks such as tracki...
Dynamics simulation can play a critical role in the engineering of robotic control code, and there exist a variety of strategies both for building physical models and for interact...