Efficient and robust trajectories play a crucial role in contact-rich manipulation, which demands accurate mod- eling of object-robot interactions. Many existing approaches rely on point contact models due to their computational effi- ciency. Simple contact models are computationally efficient but inherently limited for achieving human-like, contact-rich ma- nipulation, as they fail to capture key frictional dynamics and torque generation observed in human manipulation. This study introduces a Force-Distributed Line Contact (FDLC) model in contact-rich manipulation and compares it against conventional point contact models. A bi-level optimization framework is constructed, in which the lower-level solves an optimization problem for contact force computation, and the upper-level optimization applies iLQR for trajectory optimization. Through this framework, the limitations of point contact are demon- strated, and the benefits of the FDLC in generating efficient and robust trajectories are established. The effectiveness of the proposed approach is validated by a box rotating task, demonstrating that FDLC enables trajectories generated via non-uniform force distributions along the contact line, while requiring lower control effort and less motion of the robot.

翻译：高效且鲁棒的轨迹在接触丰富的灵巧操作中起着至关重要的作用，这要求对物体-机器人交互进行精确建模。许多现有方法依赖于点接触模型，因其计算效率高。简单的接触模型虽然计算高效，但本质上难以实现类人的、接触丰富的灵巧操作，因为它们无法捕捉人类操作中观察到的关键摩擦动力学和扭矩生成。本研究在接触丰富的灵巧操作中引入了一种力分布线接触模型，并将其与传统的点接触模型进行了比较。构建了一个双层优化框架，其中下层通过求解优化问题计算接触力，上层优化则应用iLQR进行轨迹优化。通过该框架，论证了点接触模型的局限性，并确立了FDLC在生成高效、鲁棒轨迹方面的优势。所提方法的有效性通过一个盒子旋转任务得到验证，结果表明FDLC能够沿接触线生成非均匀力分布的轨迹，同时需要更低的控制代价和更少的机器人运动。