Learning situation-dependent costs

摘要

Real world robot tasks are so complex that it is hard to hand-tune all of the domain knowledge, especially to model the dynamics of the environment. Several research efforts focufl on applying machine learning to map learning, sensor/action mapping, and vision. The work presented in this paper explores machine learning techniques for robot planning. The goal is to use real robotic navigational execution as a data source for learning.