  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. 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 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.
