Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

In recent years, much progress has been made in learning robotic manipulation policies that can follow natural language instructions. Common approaches involve learning methods that operate on offline datasets, such as task-specific teleoperated demonstrations or on hindsight labeled robotic experience. Such methods work reasonably but rely strongly on the assumption of clean data: teleoperated demonstrations are collected with specific tasks in mind, while hindsight language descriptions rely on expensive human labeling. Recently, large-scale pretrained language and vision-language models like CLIP have been applied to robotics in the form of learning representations and planners. However, can these pretrained models also be used to cheaply impart internet-scale knowledge onto offline datasets, providing access to skills contained in the offline dataset that weren't necessarily reflected in ground truth labels? We investigate fine-tuning a reward model on a small dataset of robot interactions with crowd-sourced natural language labels and using the model to relabel instructions of a large offline robot dataset. The resulting dataset with diverse language skills is used to train imitation learning policies, which outperform prior methods by up to 30% when evaluated on a diverse set of novel language instructions that were not contained in the original dataset.