Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds

• URL: http://arxiv.org/abs/2404.12440v1
• Date: Thu, 18 Apr 2024 18:01:15 GMT
• Title: Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds
• Authors: Oliver Lemke, Zuria Bauer, René Zurbrügg, Marc Pollefeys, Francis Engelmann, Hermann Blum,
• Abstract summary: This work aims to integrate 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. We show the performance and robustness of our model in two sets of real-world experiments including dynamic object retrieval and drawer opening.
• Score: 45.87961177297602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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