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The ability for people to interact with robots and teach them new skills will be crucial to the successful application of robots in everyday human environments. In order to design agents that learn efficiently and effectively from their instruction, it is important to understand how people, that are not experts in Machine Learning or robotics, will try to teach social robots. In prior work we have...
When a human learns a new motor skill from a teacher, they learn using multiple channels: They receive high level information aurally about the skill, visual information about how another performs the skill, and at times, tactile information, from a teacher's physical guidance of the student. This research proposes a novel approach, the application of this tactile feedback through a robotic wearable...
When humans learn a new motor skill from a teacher, they learn using multiple channels. They receive high level information aurally about the skill, visual information about how another performs the skill, and at times, tactile information from the teacher's physical guidance. This research proposes a novel approach where the student receives real-time tactile feedback, simultaneously over all joints,...
While reinforcement learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional...
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