Contact-rich manipulation tasks with stiff frictional elements like connector insertion are difficult to model with rigid-body simulators. In this work, we propose a new approach for modeling these environments by learning a quasi-static contact force model instead of a full simulator. Using a feature vector that contains information about the configuration and control, we find a linear mapping adequately captures the relationship between this feature vector and the sensed contact forces. A novel Linear Model Learning (LML) algorithm is used to solve for the globally optimal mapping in real time without any matrix inversions, resulting in an algorithm that runs in nearly constant time on a GPU as the model size increases. We validate the proposed approach for connector insertion both in simulation and hardware experiments, where the learned model is combined with an optimization-based controller to achieve smooth insertions in the presence of misalignments and uncertainty. Our website featuring videos, code, and more materials is available at https://model-based-plugging.github.io/.

翻译：含刚性摩擦元件的密集接触操作任务（如连接器插拔）难以通过刚体仿真器精确建模。本文提出一种通过学习准静态接触力模型而非完整仿真器来建模此类环境的新方法。我们利用包含构型与控制信息的特征向量，发现线性映射足以刻画该特征向量与感知接触力之间的关系。基于新型线性模型学习（LML）算法，可在无需矩阵求逆的情况下实时求解全局最优映射，该算法在GPU上随模型规模增大保持近似恒定运行时间。本文通过仿真与硬件实验验证了连接器插拔方法的有效性：将学习模型与基于优化的控制器相结合，可在存在对准偏差和不确定性的条件下实现平滑插拔。相关视频、代码及更多资料请访问 https://model-based-plugging.github.io/。