Contact-Implicit Model Predictive Control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach.

Dexterous in-hand manipulation is an essential skill of production and life. However, the highly stiff and mutable nature of contacts limits real-time contact detection and inference, degrading the performance of model-based methods. Inspired by recent advances in contact-rich locomotion and manipulation, this paper proposes a novel model-based approach to control dexterous in-hand manipulation and overcome the current limitations. The proposed approach has an attractive feature, which allows the robot to robustly perform long-horizon in-hand manipulation without predefined contact sequences or separate planning procedures. Specifically, we design a high-level contact-implicit model predictive controller to generate real-time contact plans executed by the low-level tracking controller. Compared to other model-based methods, such a long-horizon feature enables replanning and robust execution of contact-rich motions to achieve large displacements in-hand manipulation more efficiently; Compared to existing learning-based methods, the proposed approach achieves dexterity and also generalizes to different objects without any pre-training. Detailed simulations and ablation studies demonstrate the efficiency and effectiveness of our method. It runs at 20Hz on the 23-degree-of-freedom, long-horizon, in-hand object rotation task.