论文标题
从哪里开始？将简单技能转移到复杂的环境中
Where To Start? Transferring Simple Skills to Complex Environments
论文作者
论文摘要
机器人学习提供了多种教授机器人简单技能的方法，例如抓握。但是，这些技能通常在开放的，无杂乱的环境中进行训练，因此可能会在更复杂，混乱的环境中引起不良的碰撞。在这项工作中，我们介绍了基于环境的图表表示的负担模型，该模型在部署期间进行了优化，以找到合适的机器人配置以启动技能，以便可以在没有任何碰撞的情况下执行该技能。我们证明，我们的方法可以将先验获得的技能推广到以前看不见的混乱和约束环境，在模拟和现实世界中，以掌握和放置任务。
Robot learning provides a number of ways to teach robots simple skills, such as grasping. However, these skills are usually trained in open, clutter-free environments, and therefore would likely cause undesirable collisions in more complex, cluttered environments. In this work, we introduce an affordance model based on a graph representation of an environment, which is optimised during deployment to find suitable robot configurations to start a skill from, such that the skill can be executed without any collisions. We demonstrate that our method can generalise a priori acquired skills to previously unseen cluttered and constrained environments, in simulation and in the real world, for both a grasping and a placing task.
