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Found 8 papers, 4 papers with code
Uncertainty Propagation through Trained Deep Neural Networks Using Factor Graphs
Our implementation balances the benefits of sampling and analytical propagation techniques, which we believe, is a key factor in achieving performance improvements.
Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions
Results from our algorithmic evaluation affirm our model design choices, and the results of our user studies with non-experts support the need for the proposed inference reconciliation framework.
Towards Robust One-shot Task Execution using Knowledge Graph Embeddings
We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
Continual Learning of Knowledge Graph Embeddings
In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications.
CAGE: Context-Aware Grasping Engine
Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks.
Robotics
Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings
Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution.
Path Ranking with Attention to Type Hierarchies
The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base.
RoboCSE: Robot Common Sense Embedding
Autonomous service robots require computational frameworks that allow them to generalize knowledge to new situations in a manner that models uncertainty while scaling to real-world problem sizes.
