  We introduce a sample-efficient method for learning state-dependent stiffness
control policies for dexterous manipulation. The ability to control stiffness
facilitates safe and reliable manipulation by providing compliance and
robustness to uncertainties. So far, most current reinforcement learning
approaches to achieve robotic manipulation have exclusively focused on position
control, often due to the difficulty of learning high-dimensional stiffness
control policies. This difficulty can be partially mitigated via policy
guidance such as in imitation learning. However, expert stiffness control
demonstrations are often expensive or infeasible to record. Therefore, we
present an approach to learn Stiffness Control from Augmented Position control
Experiences (SCAPE) that bypasses this difficulty by transforming position
control demonstrations into approximate, suboptimal stiffness control
demonstrations. Then, the suboptimality of the augmented demonstrations is
addressed by using complementary techniques that help the agent safely learn
from both the demonstrations and reinforcement learning. By using simulation
tools and experiments on a robotic testbed, we show that the proposed approach
efficiently learns safe manipulation policies and outperforms learned position
control policies and several other baseline learning algorithms.
