Learning to Place New Objects
The ability to place objects in an 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.
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- Hallucinating Humans for Learning Robotic Placement of Objects, Yun Jiang, Ashutosh Saxena. In International Symposium on Experimental Robotics (ISER), 2012. [PDF]
- Learning Object Arrangements in 3D Scenes using Human Context, Yun Jiang, Marcus Lim, Ashutosh Saxena. In International Conference of Machine Learning (ICML), 2012. [PDF]
- Learning to Place New Objects in a Scene, Yun Jiang, Marcus Lim, Changxi Zheng, Ashutosh Saxena. In International Journal of Robotics Research (IJRR), 2012. [PDF]
- Learning to Place New Objects, Yun Jiang, Changxi Zheng, Marcus Lim, Ashutosh Saxena. In International Conference on Robotics and Automation (ICRA), 2012. First appeared in RSS workshop on mobile manipulation, June 2011. [PDF, slides]
|Ashutosh Saxena||asaxena at cs.cornell.edu|
|Yun Jiang||yunjiang at cs.cornell.edu|
|Changxi Zheng||cxzheng at cs.cornell.edu|
See Personal Robotics team.