DexCtrl: Sim-To-Real Dexterity with Adaptive Controller Learning

Abstract

Dexterous manipulation has advanced rapidly, with policies now capable of performing complex, contact-rich tasks in simulation. However, transferring these policies from simulation to real world remains a significant challenge. A key obstacle is the mismatch in low-level controller dynamics, where same trajectories can produce vastly different contact forces and behaviors when control parameters change. Existing solutions often rely on manual tuning or controller randomization, which can be labor-intensive, task-specific, and introduce substantial training difficulty. In this work, we propose DexCtrl, a novel framework that jointly learns actions and controller parameters by leveraging the historical information of both trajectory and controller. This adaptive controller adjustment mechanism enables the policy to automatically tune control parameters during execution, thereby mitigating severe sim-to-real gap without extensive manual tuning or excessive randomization. Moreover, by explicitly providing controller parameters as part of the observation, our approach facilitates better reasoning over force interactions and improves robustness in real-world scenarios. Experimental results demonstrate that our method achieves improved transfer performance across a variety of dexterous tasks involving variable force conditions.