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Rapid Motor Adaptation for Robotic Manipulator Arms
Drawing inspiration from RMA in locomotion and in-hand rotation, we use depth perception to develop agents tailored for rapid motor adaptation in a variety of manipulation tasks.
Learning to Infer 3D Shape Programs with Differentiable Renderer
Given everyday artifacts, such as tables and chairs, humans recognize high-level regularities within them, such as the symmetries of a table, the repetition of its legs, while possessing low-level priors of their geometries, e. g., surfaces are smooth and edges are sharp.
Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings
We study a way of combing two of the most successful routes to meaning of language--statistical language models and symbolic semantics formalisms--in the task of semantic parsing.
Drawing out of Distribution with Neuro-Symbolic Generative Models
We then adopt a subset of the Omniglot challenge tasks, and evaluate its ability to generate new exemplars (both unconditionally and conditionally), and perform one-shot classification, showing that DooD matches the state of the art.
