Learning Hierarchical Control for Robust In-Hand Manipulation

Tingguang Li 1, 2, Krishnan Srinivasan 2, Max Qing-Hu Meng 1, Wenzhen Y uan 3 and Jeannette Bohg 2 Abstract -- Robotic in-hand manipulation has been a longstanding challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipulation sequences. T o address these challenges, the majority of prior work has either focused on model-based, low-level controllers or on model-free deep reinforcement learning that each have their own limitations. We propose a hierarchical method that relies on traditional, model-based controllers on the low-level and learned policies on the mid-level. The low-level controllers can robustly execute different manipulation primitives (reposing, sliding, flipping). We extensively evaluate our approach in simulation with a 3-fingered hand that controls three degrees of freedom of elongated objects. We show that our approach can move objects between almost all the possible poses in the workspace while keeping them firmly grasped. We also show that our approach is robust to inaccuracies in the object models and to observation noise. Finally, we show how our approach generalizes to objects of other shapes. I NTRODUCTION Dexterous Manipulation refers to the ability of changing the pose of an object to any other pose within the workspace of a hand [1-3]. In this paper, we are particularly concerned with the ability of in-hand manipulation where the object is continuously moved within the hand without dropping. This ability is used frequently in human manipulation e.g. when grasping a tool and readjusting it within the hand, when inspecting an object, when assembling objects or when adjusting an unstable grasp. Y et, in-hand manipulation remains a longstanding challenge in robotics despite the availability of multi-fingered dexterous hands such as [4-6].