Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)

Abstract

Recent 3D generative models, which are capable of generating full object shapes from just a few images, now open up new opportunities in robotics. In this work, we show that 3D generative models can be used to augment a dataset from a single real-world demonstration, after which an omnidirectional policy can be learned within this imagined dataset. We found that this enables a robot to perform a task when initialised from states very far from those observed during the demonstration, including starting from the opposite side of the object to the real-world demonstration, significantly reducing the number of demonstrations required for policy learning. Through several real-world experiments across tasks such as grasping objects, opening a drawer, and placing trash into a bin, we study these omnidirectional policies by investigating the effect of various design choices on policy behaviour, and we show superior performance to recent baselines which use alternative methods for data augmentation. 

3D Generation Gallery

We use a 3D generative model to synthesise the target object. The first row displays all the input images provided to the 3D generation module, while the second row shows the resulting generated object. As illustrated, even with limited input views, the 3D generative model is capable of producing plausible novel renderings, enabling our OP-Gen to augment the dataset in an omnidirectional manner.

Robot Videos