  Dexterous in-hand manipulation is an essential skill of production and life.
Nevertheless, the highly stiff and mutable features of contacts cause
limitations to real-time contact discovery and inference, which degrades the
performance of model-based methods. Inspired by recent advancements 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 the attractive feature, which
allows the robot to robustly execute long-horizon in-hand manipulation without
pre-defined contact sequences or separated planning procedures. Specifically,
we design a contact-implicit model predictive controller at high-level to
generate real-time contact plans, which are executed by the low-level tracking
controller. Compared with other model-based methods, such a long-horizon
feature enables replanning and robust execution of contact-rich motions to
achieve large-displacement in-hand tasks more efficiently; Compared with
existing learning-based methods, the proposed approach achieves the 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.
