论文标题
GOMP-ST：掌握吸入运输的优化运动计划
GOMP-ST: Grasp Optimized Motion Planning for Suction Transport
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Suction cup grasping is very common in industry, but moving too quickly can cause suction cups to detach, causing drops or damage. Maintaining a suction grasp throughout a high-speed motion requires balancing suction forces against inertial forces while the suction cups deform under strain. In this paper, we consider Grasp Optimized Motion Planning for Suction Transport (GOMP-ST), an algorithm that combines deep learning with optimization to decrease transport time while avoiding suction cup failure. GOMP-ST first repeatedly moves a physical robot, vacuum gripper, and a sample object, while measuring pressure with a solid-state sensor to learn critical failure conditions. Then, these are integrated as constraints on the accelerations at the end-effector into a time-optimizing motion planner. The resulting plans incorporate real-world effects such as suction cup deformation that are difficult to model analytically. In GOMP-ST, the learned constraint, modeled with a neural network, is linearized using Autograd and integrated into a sequential quadratic program optimization. In 420 experiments with a physical UR5 transporting objects ranging from 1.3 to 1.7 kg, we compare GOMP-ST to baseline optimizing motion planners. Results suggest that GOMP-ST can avoid suction cup failure while decreasing transport times from 16% to 58%. For code, video, and datasets, see https://sites.google.com/view/gomp-st.
