Learning from Demonstration of Pouring

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LfD Pouring

Learning from Demonstration of Pouring

This is a case study of "pouring" under the learning from demonstration (LfD) framework.

The goal of this work is to explore how to represent, plan, and learn complex tasks that have many variations. We want to enable robots to go beyond manipulating rigid bodies. Pouring, a form of manipulating liquids and granular materials, is an example of such a task. Materials we pour range from water to more viscous liquids such as shampoo. In making a pizza (see below video), a number of pouring skills are used: pouring cheese from a bag, pouring vegetables from a bowl, pouring tomato sauce with shaking or squeezing a bottle, pouring seasonings, and so on. Pouring can involve tipping, shaking, and tapping a container. Pouring has different variations involving many materials, container shapes, contexts, initial poses of containers, target amounts, and obstacles. In order to handle these variations, we humans use many strategies or skills.

Video: Human is making a pizza where many skills are used.

Conceptual illustration of LfD scheme
Fig: Conceptual illustration of our learning from demonstration scheme. Left: learning from human demonstrations, Right: behavior generation and refinement.

The most important finding of this case study is that in order to model a behavior with wide generalization, it is a practical solution to store small skills (e.g. tipping, shaking, grasping) in a library, and combine them for an entire task, using planning and learning methods for selection and adjustment (see Fig.). Though we cannot say this approach is the best, its practicality is verified by the robot experiments, using a PR2 robot (see following video).

Video: Pouring skills by PR2.

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