Learning Temporally Composable Task Segmentations with Language

In this work, we present an approach to identify sub-tasks within a demonstrated robot trajectory with the supervision provided by language instructions. Learning longer horizon tasks is challenging with techniques such as reinforcement learning and behavior cloning. Previous approaches have split these long tasks into shorter tasks that are easier to learn by using statistical change point detection methods. However, classical changepoint detection methods function only with low dimensional robot trajectory data and not with high dimensional inputs such as vision. Our goal in this work is to split longer horizon tasks, represented by trajectories into shorter horizon tasks that can be learned using conventional behavior cloning approaches using guidance from language. In our approach we use techniques from the video moment retrieval problem on robot trajectory data to demonstrate a high-dimensional generalizable change-point detection approach. Our proposed moment retrieval-based approach shows a more than 30% improvement in mean average precision (mAP) for identifying trajectory sub-tasks with language guidance compared to that without language. We perform ablations to understand the effects of domain randomization, sample complexity, views, and sim-to-real transfer of our method. In our data ablation we find that just with a 100 labelled trajectories we can achieve a 61.41 mAP, demonstrating the sample efficiency of using such an approach. Further, behavior cloning models trained on our segmented trajectories outperform a single model trained on the whole trajectory by up to 20%.