ロボット運動計画と制御のための微分可能な制約付き模倣学習
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control
模倣学習の柔軟性と最適制御のハード制約を微分可能な形で統合し、移動ロボットや自動運転の運動計画・制御を安全に学習する枠組みを提案した。
著者: Christopher Diehl, Janis Adamek, Martin Krüger, Frank Hoffmann, Torsten Bertram
分類: cs.RO, cs.LG
原文アブストラクト
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.
