In the communicative learning, language acquisition and teaching using language are carried out in the same autonomous mode. We show how the SAIL robot learns simple spoken instructions interactively and how humans use spoken commands to interactively teach the robot to manipulate objects and to perform vision-guided navigation.

In this talk, we discuss the nature of uncertainty, in general, and its relevance to various problems found in the design of intelligent autonomous agents, in particular. We present first, in the context of a basic unifying model, basic epistemic notions of uncertainty, evidence, and ignorance, focusing on the explanation of probabilistic and non-probabilistic approaches to measure and characterize uncertainty.

We discuss next several situations and tasks where mobile autonomous agents must cope with probabilistic and non-probabilistic uncertainty. We focus particularly on non-probabilistic approaches to uncertain reasoning and explain the connections between this family of methods with logics of utility and with measures of uncertainty like the radius of information of Traub and Wozniakowski.

We present several examples of application of techniques inspired on these ideas to planning and control of individual robots and of teams of collaborating mobile agents. We employ these examples to discuss the interrelation of communication, perception, motion, and other actions in the performance of robotic tasks.