通过接触力分布进行操控
Manipulation via Force Distribution at Contact
AI总结：
本研究提出力分布线接触模型，通过双层优化框架实现高效稳健的接触丰富操控，验证了其在非均匀力分布下生成轨迹的优势。
摘要：
高效的和稳健的轨迹在接触丰富的操控中起着至关重要的作用，这要求对物体-机器人相互作用进行准确的建模。许多现有的方法依赖于点接触模型，因为它们计算效率高。简单的接触模型计算效率高，但本质上限制了实现类人、接触丰富的操控，因为它们无法捕捉到人类操控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操控中引入了力分布线接触（FDLC）模型，并将其与传统点接触模型进行比较。构建了一个双层优化框架，在其中下层解决接触力计算的优化问题，上层优化则应用iLQR进行轨迹优化。通过这个框架，点接触的局限性得到了展示，而FDLC在生成高效和稳健的轨迹方面的优势也得到了确立。通过一个箱子旋转任务验证了所提出方法的有效性，证明FDLC能够通过接触线上的非均匀力分布生成轨迹，同时要求更低的控制努力和更少的机器人运动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
