  Large-scale training have propelled significant progress in various
sub-fields of AI such as computer vision and natural language processing.
However, building robot learning systems at a comparable scale remains
challenging. To develop robots that can perform a wide range of skills and
adapt to new scenarios, efficient methods for collecting vast and diverse
amounts of data on physical robot systems are required, as well as the
capability to train high-capacity policies using such datasets. In this work,
we propose a framework for scaling robot learning, with specific focus on
multi-task and multi-scene manipulation in kitchen environments, both in
simulation and in the real world. Our proposed framework, CACTI, comprises four
stages that separately handle: data collection, data augmentation, visual
representation learning, and imitation policy training, to enable scalability
in robot learning . We make use of state-of-the-art generative models as part
of the data augmentation stage, and use pre-trained out-of-domain visual
representations to improve training efficiency. Experimental results
demonstrate the effectiveness of our approach. On a real robot setup, CACTI
enables efficient training of a single policy that can perform 10 manipulation
tasks involving kitchen objects, and is robust to varying layouts of
distractors. In a simulated kitchen environment, CACTI trains a single policy
to perform 18 semantic tasks across 100 layout variations for each individual
task. We will release the simulation task benchmark and augmented datasets in
both real and simulated environments to facilitate future research.
