面向机器人运动规划与控制的可微约束模仿学习
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control
AI总结：
针对机器人运动规划中软约束模仿学习在分布外场景易出安全故障的问题，提出融合硬约束的可微模仿学习框架，经仿真实验验证了其在移动机器人与自动驾驶场景的有效性。
摘要：
运动规划与控制是自动驾驶等机器人应用的核心组件。系统动力学、安全边界（如障碍物）等时空硬约束会限制机器人的运动。最优控制中的直接法通过求解带约束的优化问题实现规划，但在许多应用中，由于部分目标存在冲突需要权衡权重，很难找到合适的代价函数。另一方面，行为克隆（BC）等模仿学习（IL）方法为从离线演示中学习决策提供了直观框架，是复杂机器人应用中规划与控制的可行方向。现有研究主要依赖软约束方法，通过额外的辅助损失项描述约束，但在分布外（OOD）场景下可能发生灾难性的安全关键故障。本研究将IL的灵活性与最优控制中的硬约束处理能力相结合，提出了一个适用于约束机器人运动规划与控制、交通智能体仿真的通用框架，重点面向移动机器人与自动驾驶场景。我们通过显式补全和基于梯度的校正，以可微的方式将硬约束融入学习问题。移动机器人导航与自动驾驶的仿真实验验证了所提方法的性能。
英文摘要：
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.
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