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Found 4 papers, 2 papers with code
Discovering Predictive Relational Object Symbols with Symbolic Attentive Layers
We compare the performance of our proposed architecture with state-of-the-art symbol discovery methods in a simulated tabletop environment where the robot needs to discover symbols related to the relative positions of objects to predict the observed effect successfully.
Graph2Pix: A Graph-Based Image to Image Translation Framework
In this paper, we propose a graph-based image-to-image translation framework for generating images.
High-level Features for Resource Economy and Fast Learning in Skill Transfer
To be concrete, in our simulation experiments, we either apply principal component analysis (PCA) or slow feature analysis (SFA) on the signals collected from the last hidden layer of a deep network while it performs a source task, and use these features for skill transfer in a new target task.
DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning
We propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning.
