Robots need to be able to learn concepts from their users in order to adapt their capabilities to each user's unique task. But when the robot operates on high-dimensional inputs, like images or point clouds, this is impractical: the robot needs an unrealistic amount of human effort to learn the new concept. To address this challenge, we propose a new approach whereby the robot learns a low-dimensional variant of the concept and uses it to generate a larger data set for learning the concept in the high-dimensional space. This lets it take advantage of semantically meaningful privileged information only accessible at training time, like object poses and bounding boxes, that allows for richer human interaction to speed up learning. We evaluate our approach by learning prepositional concepts that describe object state or multi-object relationships, like above, near, or aligned, which are key to user specification of task goals and execution constraints for robots. Using a simulated human, we show that our approach improves sample complexity when compared to learning concepts directly in the high-dimensional space. We also demonstrate the utility of the learned concepts in motion planning tasks on a 7-DoF Franka Panda robot.
翻译:机器人需要能够向用户学习概念, 以便让自己的能力适应每个用户的独特任务。 但是, 当机器人在高维投入上操作时, 比如图像或点云, 这不切实际: 机器人需要大量不切实际的人类努力来学习新概念。 为了应对这一挑战, 我们提议一种新的方法, 机器人可以学习概念的低维变量, 并用它来生成更大的数据集, 用于在高维空间学习概念。 这样它才能利用在训练时可以获取的具有意义的精密特惠信息, 比如物体的配置和捆绑盒, 从而让人类更丰富的互动来加速学习。 我们通过学习描述物体状态或多点关系的预定位概念来评估我们的方法, 比如上面、 附近 或 校准, 这些概念是用户对任务目标和机器人执行限制做出说明的关键。 使用模拟人类, 我们显示我们的方法在与直接在高维空间学习概念时提高了样本的复杂性。 我们还展示了7 - DoFranka Panda 机器人在运动规划任务中学习的概念的实用性 。