106 research outputs found
HoME: a Household Multimodal Environment
We introduce HoME: a Household Multimodal Environment for artificial agents
to learn from vision, audio, semantics, physics, and interaction with objects
and other agents, all within a realistic context. HoME integrates over 45,000
diverse 3D house layouts based on the SUNCG dataset, a scale which may
facilitate learning, generalization, and transfer. HoME is an open-source,
OpenAI Gym-compatible platform extensible to tasks in reinforcement learning,
language grounding, sound-based navigation, robotics, multi-agent learning, and
more. We hope HoME better enables artificial agents to learn as humans do: in
an interactive, multimodal, and richly contextualized setting.Comment: Presented at NIPS 2017's Visually-Grounded Interaction and Language
Worksho
Stepwise Acquisition of Dialogue Act Through Human-Robot Interaction
A dialogue act (DA) represents the meaning of an utterance at the
illocutionary force level (Austin 1962) such as a question, a request, and a
greeting. Since DAs take charge of the most fundamental part of communication,
we believe that the elucidation of DA learning mechanism is important for
cognitive science and artificial intelligence. The purpose of this study is to
verify that scaffolding takes place when a human teaches a robot, and to let a
robot learn to estimate DAs and to make a response based on them step by step
utilizing scaffolding provided by a human. To realize that, it is necessary for
the robot to detect changes in utterance and rewards given by the partner and
continue learning accordingly. Experimental results demonstrated that
participants who continued interaction for a sufficiently long time often gave
scaffolding for the robot. Although the number of experiments is still
insufficient to obtain a definite conclusion, we observed that 1) the robot
quickly learned to respond to DAs in most cases if the participants only spoke
utterances that match the situation, 2) in the case of participants who builds
scaffolding differently from what we assumed, learning did not proceed quickly,
and 3) the robot could learn to estimate DAs almost exactly if the participants
kept interaction for a sufficiently long time even if the scaffolding was
unexpected.Comment: Published as a conference paper at IJCNN 201
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