CAGE: Context-Aware Grasping Engine

Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks. In order for robots to effectively perform object manipulation, a broad sense of contexts, including object and task constraints, needs to be accounted for. We introduce the Context-Aware Grasping Engine, which combines a novel semantic representation of grasp contexts with a neural network structure based on the Wide & Deep model, capable of capturing complex reasoning patterns. We quantitatively validate our approach against three prior methods on a novel dataset consisting of 14, 000 semantic grasps for 44 objects, 7 tasks, and 6 different object states. Our approach outperformed all baselines by statistically significant margins, producing new insights into the importance of balancing memorization and generalization of contexts for semantic grasping. We further demonstrate the effectiveness of our approach on robot experiments in which the presented model successfully achieved 31 of 32 suitable grasps. The code and data are available at: https://github.com/wliu88/railsemanticgrasping.

Authors

• Weiyu Liu
• Angel Andres Daruna
• Sonia Chernova

Keywords

• Semantics
• Task analysis
• Grasping
• Feature extraction
• Robots
• Cognition
• Context modeling
• Neural Network
• Deep Models
• Objective Conditions
• Semantic Representations
• Prior Methods
• Task Constraints
• Robot Experiments
• Training Set
• Contextual Information
• General Function
• Visual Features
• Point Cloud
• Semantic Information
• Feed-forward Network
• Object Classification
• Low-level Features
• Semantic Features
• Rating Task
• Abstract Representations
• Mean Average Precision
• Extract Semantic Features
• Object Instances
• Embedding Vectors
• Object Point Cloud
• Pooling Function
• Sparse Feature
• Everyday Objects
• Object Parts
• Model Discrimination
• Average Precision