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Found 3 papers, 2 papers with code
Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories
We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e. g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot.
Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios
In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking.
Class Teaching for Inverse Reinforcement Learners
In this paper we propose the first machine teaching algorithm for multiple inverse reinforcement learners.
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