SpotLight: Robotic Scene Understanding through Interaction and Affordance Detection
Tim Engelbracht, Ren\'e Zurbr\"ugg, Marc Pollefeys, Hermann, Blum, Zuria Bauer

TL;DR
SpotLight is a framework that enables robots to understand and interact with functional household elements like light switches through affordance detection and physical interaction, improving scene understanding and task execution.
Contribution
The paper introduces a novel framework combining affordance prediction, interaction, and scene understanding, with a new dataset and detection model for light switches, extending to other functional elements.
Findings
Achieved up to 84% success in real-world light switch operation
Developed a new dataset with 715 images for light switch detection
Demonstrated robot learning through interaction and scene graph exploration
Abstract
Despite increasing research efforts on household robotics, robots intended for deployment in domestic settings still struggle with more complex tasks such as interacting with functional elements like drawers or light switches, largely due to limited task-specific understanding and interaction capabilities. These tasks require not only detection and pose estimation but also an understanding of the affordances these elements provide. To address these challenges and enhance robotic scene understanding, we introduce SpotLight: A comprehensive framework for robotic interaction with functional elements, specifically light switches. Furthermore, this framework enables robots to improve their environmental understanding through interaction. Leveraging VLM-based affordance prediction to estimate motion primitives for light switch interaction, we achieve up to 84% operation success in real world…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Human Pose and Action Recognition · Robot Manipulation and Learning
