论文标题
可能使用运动原语的基于视觉的计划大致基于视觉的计划
Probably Approximately Correct Vision-Based Planning using Motion Primitives
论文作者
论文摘要
本文提出了一种基于学习视觉的计划者的方法，该方法可证明对新的环境（即在培训期间看不见的环境）概括。我们利用大约正确的（PAC） - 贝斯框架来获得所有环境中政策的预期成本的上限。最小化Pac-Bayes上限，因此训练策略，这些策略伴随着新的环境证书。我们提出的训练管道通过（a）使用进化策略（ES）在政策空间上获得良好的先验分布，从而为深度神经网络政策提供了强有力的概括保证，然后（b）将PAC-Bayes优化作为一种有效溶剂的参数convex优化问题提出。我们通过两个模拟示例证明了我们的方法为学习的基于视觉的运动计划者提供强大的概括保证的功效：（1）无人驾驶飞机（UAV）用船上视觉传感器导航障碍物场，以及（2）动态的四足机器人，具有前提性和外移传感器的粗糙地形。
This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Probably Approximately Correct (PAC)-Bayes framework to obtain an upper bound on the expected cost of policies across all environments. Minimizing the PAC-Bayes upper bound thus trains policies that are accompanied by a certificate of performance on novel environments. The training pipeline we propose provides strong generalization guarantees for deep neural network policies by (a) obtaining a good prior distribution on the space of policies using Evolutionary Strategies (ES) followed by (b) formulating the PAC-Bayes optimization as an efficiently-solvable parametric convex optimization problem. We demonstrate the efficacy of our approach for producing strong generalization guarantees for learned vision-based motion planners through two simulated examples: (1) an Unmanned Aerial Vehicle (UAV) navigating obstacle fields with an onboard vision sensor, and (2) a dynamic quadrupedal robot traversing rough terrains with proprioceptive and exteroceptive sensors.
