01909nas a2200169   4500008004100000245008200041210006900123260002200192520135400214100001801568700001801586700002601604700002201630700001501652700002101667856005101688       2022                        eng d00aSCAPE: Learning Stiffness Control from Augmented Position Control Experiences0 aSCAPE Learning Stiffness Control from Augmented Position Control  bPMLRc08–11 Nov3 a<p>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.</p>
1 aKim, Mincheol1 aNiekum, Scott1 aDeshpande, Ashish, D.1 aFaust, Aleksandra1 aHsu, David1 aNeumann, Gerhard  uhttps://proceedings.mlr.press/v164/kim22b.html