Learning to Place New Objects

TL;DR

This paper presents a supervised learning approach enabling robots to place new objects in suitable locations and orientations in unstructured environments, achieving high success and preference rates even for unseen objects and areas.

Contribution

The authors introduce a novel supervised learning algorithm that predicts stable and preferred object placements using point-cloud features, generalizing to unseen objects and environments.

Findings

98% success rate in stable placement of new objects

92% of placements matched preferred configurations

Effective in unstructured, real-world environments

Abstract

The ability to place objects in the environment is an important skill for a personal robot. An object should not only be placed stably, but should also be placed in its preferred location/orientation. For instance, a plate is preferred to be inserted vertically into the slot of a dish-rack as compared to be placed horizontally in it. Unstructured environments such as homes have a large variety of object types as well as of placing areas. Therefore our algorithms should be able to handle placing new object types and new placing areas. These reasons make placing a challenging manipulation task. In this work, we propose a supervised learning algorithm for finding good placements given the point-clouds of the object and the placing area. It learns to combine the features that capture support, stability and preferred placements using a shared sparsity structure in the parameters. Even when neither the object nor the placing area is seen previously in the training set, our algorithm predicts good placements. In extensive experiments, our method enables the robot to stably place several new objects in several new placing areas with 98% success-rate; and it placed the objects in their preferred placements in 92% of the cases.