  This letter presents contact-safe Model-based Reinforcement Learning (MBRL)
for robot applications that achieves contact-safe behaviors in the learning
process. In typical MBRL, we cannot expect the data-driven model to generate
accurate and reliable policies to the intended robotic tasks during the
learning process due to sample scarcity. Operating these unreliable policies in
a contact-rich environment could cause damage to the robot and its
surroundings. To alleviate the risk of causing damage through unexpected
intensive physical contacts, we present the contact-safe MBRL that associates
the probabilistic Model Predictive Control's (pMPC) control limits with the
model uncertainty so that the allowed acceleration of controlled behavior is
adjusted according to learning progress. Control planning with such
uncertainty-aware control limits is formulated as a deterministic MPC problem
using a computation-efficient approximated GP dynamics and an approximated
inference technique. Our approach's effectiveness is evaluated through bowl
mixing tasks with simulated and real robots, scooping tasks with a real robot
as examples of contact-rich manipulation skills. (video:
https://youtu.be/sdhHP3NhYi0)
