Learning Hierarchical Control for Robust In-Hand Manipulation
This addresses the problem of robust in-hand manipulation for robotics, offering a hybrid approach that improves over prior methods but is incremental in combining existing techniques.
The paper tackles the challenge of robotic in-hand manipulation by proposing a hierarchical method combining model-based low-level controllers and learned mid-level policies, achieving robust movement of objects between almost all poses in a simulated workspace while maintaining a firm grasp.
Robotic in-hand manipulation has been a long-standing challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipulation sequences. To 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). The mid-level policy orchestrates these primitives. 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.