  Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand
multimodal perception for effective and precise task execution. However, these
present a novel challenge to robots as they lack the ability to combine these
multimodal stimuli for performing contact-rich tasks. Learning-based methods
have attempted to model multi-modal contact-rich tasks, but they often require
extensive training examples and task-specific reward functions which limits
their practicality and scope. Hence, we propose a generalizable model-free
learning-from-demonstration framework for robots to learn contact-rich skills
without explicit reward engineering. We present a novel multi-modal sensor data
representation which improves the learning performance for contact-rich skills.
We performed training and experiments using the real-life Sawyer robot for
three everyday contact-rich skills -- cleaning, writing, and peeling. Notably,
the framework achieves a success rate of 100\% for the peeling and writing
skill, and 80\% for the cleaning skill. Hence, this skill learning framework
can be extended for learning other physical manipulation skills.
