  In recent years, modern techniques in deep learning and large-scale datasets
have led to impressive progress in 3D instance segmentation, grasp pose
estimation, and robotics. This allows for accurate detection directly in 3D
scenes, object- and environment-aware grasp prediction, as well as robust and
repeatable robotic manipulation. This work aims to integrate these recent
methods into a comprehensive framework for robotic interaction and manipulation
in human-centric environments. Specifically, we leverage 3D reconstructions
from a commodity 3D scanner for open-vocabulary instance segmentation,
alongside grasp pose estimation, to demonstrate dynamic picking of objects, and
opening of drawers. We show the performance and robustness of our model in two
sets of real-world experiments including dynamic object retrieval and drawer
opening, reporting a 51% and 82% success rate respectively. Code of our
framework as well as videos are available on: https://spot-compose.github.io/.
