论文标题
无调接触式轨迹优化
Tuning-Free Contact-Implicit Trajectory Optimization
论文作者
论文摘要
我们提出了一个接触式轨迹优化框架，该框架可以使用微不足道的初始猜测来计划不同的机器人体系结构和任务的接触式轨迹，而无需任何参数调整。这是通过使用宽松的接触模型以及自动惩罚循环来抑制放松的。此外，问题的结构使我们能够利用上一个迭代中使用松弛所隐含的联系信息，从而在很少的计算开销中明确改进了解决方案。我们在模拟实验中使用7-DOF ARM和移动机器人进行非划理操作测试了所提出的方法，并使用零重力的类人形机器人进行了平面运动。结果表明，我们的方法为广泛的应用提供了良好性能的开箱即用解决方案。
We present a contact-implicit trajectory optimization framework that can plan contact-interaction trajectories for different robot architectures and tasks using a trivial initial guess and without requiring any parameter tuning. This is achieved by using a relaxed contact model along with an automatic penalty adjustment loop for suppressing the relaxation. Moreover, the structure of the problem enables us to exploit the contact information implied by the use of relaxation in the previous iteration, such that the solution is explicitly improved with little computational overhead. We test the proposed approach in simulation experiments for non-prehensile manipulation using a 7-DOF arm and a mobile robot and for planar locomotion using a humanoid-like robot in zero gravity. The results demonstrate that our method provides an out-of-the-box solution with good performance for a wide range of applications.
