Learning Dexterous Manipulation Skills from Imperfect Simulations

Figure 1: We propose DexScrew, a sim-to-real framework for learning dexterous manipulation skills when the environment cannot be accurately simulated. In simulation, we use simplified objects to learn transferable rotational skills, which are then used to collect data and train tactile policies in the real world. We demonstrate the framework on contact-rich screwdriving (top row) and nut-bolt fastening (middle row). We also show generalization across different objects (bottom row). More videos and code are available on https://dexscrew.github.io. Abstract-- Reinforcement learning and sim-to-real transfer have made significant progress in dexterous manipulation. However, progress remains limited by the difficulty of simulating complex contact dynamics and multisensory signals, especially tactile feedback. In this work, we propose DexScrew, a sim-to-real framework that addresses these limitations and demonstrates its effectiveness on nut-bolt fastening and screwdriving with multi-fingered hands. The framework has three stages. First, we train reinforcement learning policies in simulation using simplified object models that lead to the emergence of correct finger gaits. We then use the learned policy as a skill primitive within a teleoperation system to collect real-world demonstrations that contain tactile and proprioceptive information. Finally, we train a behavior cloning policy that incorporates tactile sensing and show that it generalizes to nuts and screwdrivers with diverse geometries. Experiments across both tasks show high task progress ratios compared to direct sim-to-real transfer and robust performance even on unseen object shapes and under external perturbations.