RoboAgent: Towards Sample Efficient Robot Manipulation with Semantic Augmentations and Action Chunking

The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets is strenuous due to manual efforts, operational costs, and safety challenges. A path toward such a universal agent requires an efficient framework capable of generalization but within a reasonable data budget. In this paper, we develop an efficient framework (MT-ACT) for training universal agents capable of multi-task manipulation skills using (a) semantic augmentations that can rapidly multiply existing datasets and (b) action representations that can extract performant policies with small yet diverse multi-modal datasets without overfitting. In addition, reliable task conditioning and an expressive policy architecture enables our agent to exhibit a diverse repertoire of skills in novel situations specified using task commands. Using merely 7500 demonstrations, we are able to train a single policy RoboAgent capable of 12 unique skills, and demonstrate its generalization over 38 tasks spread across common daily activities in diverse kitchen scenes. On average, RoboAgent outperforms prior methods by over 40% in unseen situations while being more sample efficient.