Motion planning and control are crucial components of robotics applications like automated driving. Here, spatio-temporal hard constraints like system dynamics and safety boundaries (e.g., obstacles) restrict the robot's motions. Direct methods from optimal control solve a constrained optimization problem. However, in many applications finding a proper cost function is inherently difficult because of the weighting of partially conflicting objectives. On the other hand, Imitation Learning (IL) methods such as Behavior Cloning (BC) provide an intuitive framework for learning decision-making from offline demonstrations and constitute a promising avenue for planning and control in complex robot applications. Prior work primarily relied on soft constraint approaches, which use additional auxiliary loss terms describing the constraints. However, catastrophic safety-critical failures might occur in out-of-distribution (OOD) scenarios. This work integrates the flexibility of IL with hard constraint handling in optimal control. Our approach constitutes a general framework for constraint robotic motion planning and control, as well as traffic agent simulation, whereas we focus on mobile robot and automated driving applications. Hard constraints are integrated into the learning problem in a differentiable manner, via explicit completion and gradient-based correction. Simulated experiments of mobile robot navigation and automated driving provide evidence for the performance of the proposed method.

翻译：运动规划与控制是自动驾驶等机器人应用中的关键组成部分。在此类应用中，系统动力学和安全性边界（如障碍物）等时空硬约束限制了机器人的运动。最优控制中的直接方法可求解约束优化问题。然而，在许多应用中，由于部分冲突目标的权重分配，寻找合适的代价函数本质上是困难的。另一方面，行为克隆等模仿学习方法为从离线演示中学习决策制定提供了直观框架，并构成复杂机器人应用中规划与控制的有前景途径。先前研究主要依赖软约束方法，通过引入描述约束的额外辅助损失项。但在分布外场景中可能发生灾难性的安全关键故障。本研究将模仿学习的灵活性与最优控制中的硬约束处理相融合。该方法构成了约束机器人运动规划与控制以及交通智能体仿真的通用框架，重点聚焦于移动机器人与自动驾驶应用。通过显式补全和基于梯度的修正，以可微分方式将硬约束纳入学习问题。移动机器人导航与自动驾驶的仿真实验为所提方法的性能提供了证据。