We address the problem of learning static spatial representation of a robot motor system and the environment to solve a general forward/inverse kinematics problem. The latter proves complex for high degree-of-freedom systems. The proposed architecture relates to a recent research in cognitive science, which provides a solid evidence that perception and action share common neural architectures. We propose to model both a motor system and an environment with compositional hierarchies and develop an algorithm for learning them together with a mapping between the two. We show that such a representation enables efficient learning and inference of robot states. We present our experiments in a simulated environment and with a humanoid robot Nao.
COBISS.SI-ID: 10212692
The research question addressed in this paper is: Given a problem, can we automatically predict how difficult the problem will be to solve by humans? We focus our investigation on problems in which the difficulty arises from the combinatorial complexity of problems. We propose a measure of difficulty that is based on modeling the problem solving effort as search among alternatives and the relations among alternative solutions. In experiments in the chess domain, using data obtained from very strong human players, this measure was shown at a high level of statistical significance to be adequate as a genuine measure of difficulty for humans.
COBISS.SI-ID: 10025300
We proposed Error reduction merging (ERM), a new learning method that automatically discovers similarities in the structure of the agent’s environment. ERM identifies different types of objects solely from the data measured and merges the observations of objects that behave in the same or similar way in order to speed up the agent’s learning. We performed a series of experiments in worlds of increasing complexity. The robot had to learn a qualitative model predicting the change in the robot’s distance to an object. The results indicate that ERM was capable of discovering structural similarities in the data which indeed made the learning faster, clearly superior to conventional learning. This observed trend occurred with various machine learning algorithms used inside the ERM method.
COBISS.SI-ID: 9731668