Planning over discontinuous dynamics is needed for robotics tasks like contact-rich manipulation, which presents challenges in the numerical stability and speed of planning methods when either neural network or analytical models are used. On the one hand, sampling-based planners require higher sample complexity in high-dimensional problems and cannot describe safety constraints such as force limits. On the other hand, gradient-based solvers can suffer from local optima and convergence issues when the Hessian is poorly conditioned. We propose a planning method with both sampling- and gradient-based elements, using the Cross-entropy Method to initialize a gradient-based solver, providing better search over local minima and the ability to handle explicit constraints. We show the approach allows smooth, stable contact-rich planning for an impedance-controlled robot making contact with a stiff environment, benchmarking against gradient-only MPC and CEM.

翻译：在机器人接触丰富型操作等任务中，需对非连续动力学进行规划，这给使用神经网络或解析模型的规划方法带来了数值稳定性与速度方面的挑战。一方面，基于采样的规划器在高维问题中样本复杂度较高，且无法描述力约束等安全限制。另一方面，基于梯度的求解器在Hessian矩阵条件数较差时可能陷入局部最优或收敛困难。本文提出一种融合采样与梯度元素的规划方法：采用交叉熵方法初始化梯度求解器，从而更有效地搜索局部极小值并处理显式约束。实验表明，该方法能实现阻抗控制机器人与刚性环境接触时的平滑稳定规划，并与纯梯度MPC及CEM基准方法进行了对比。