  Humans naturally "program" a fellow collaborator to perform a task by
demonstrating the task few times. It is intuitive, therefore, for a human to
program a collaborative robot by demonstration and many paradigms use a single
demonstration of the task. This is a form of one-shot learning in which a
single training example, plus some context of the task, is used to infer a
model of the task for subsequent execution and later refinement. This paper
presents a one-shot learning from demonstration framework to learn
contact-intensive tasks using only visual perception of the demonstrated task.
The robot learns a policy for performing the tasks in terms of a priori skills
and further uses self-evaluation based on visual and tactile perception of the
skill performance to learn the force correspondences for the skills. The
self-evaluation is performed based on goal states detected in the demonstration
with the help of task context and the skill parameters are tuned using
reinforcement learning. This approach enables the robot to learn force
correspondences which cannot be inferred from a visual demonstration of the
task. The effectiveness of this approach is evaluated using a vegetable peeling
task.
