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Deep compositional robotic planners that follow natural language commands
(Center for Brains, Minds and Machines (CBMM), Computation and Systems Neuroscience (Cosyne), 2020-05-31)
We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and manipu- late objects. Our approach combines ...
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
(Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR), 2020-10-25)
We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LTL formulas have never been seen before, ...
Learning a natural-language to LTL executable semantic parser for grounded robotics
(Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020-11-16)
Children acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct ...