A Multimodal Human-Robot Interaction Dataset

Abstract : This works presents a multimodal dataset for Human-Robot Interactive Learning. 1 The dataset contains synchronized recordings of several human users, from a stereo 2 microphone and three cameras mounted on the robot. The focus of the dataset is 3 incremental object learning, oriented to human-robot assistance and interaction. To 4 learn new object models from interactions with a human user, the robot needs to 5 be able to perform multiple tasks: (a) recognize the type of interaction (pointing, 6 showing or speaking), (b) segment regions of interest from acquired data (hands and 7 objects), and (c) learn and recognize object models. We illustrate the advantages 8 of multimodal data over camera-only datasets by presenting an approach that 9 recognizes the user interaction by combining simple image and language features.
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NIPS 2016, workshop Future of Interactive Learning Machines, Dec 2016, Barcelona, Spain
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Pablo Azagra, Yoan Mollard, Florian Golemo, Ana Cristina Murillo, Manuel Lopes, et al.. A Multimodal Human-Robot Interaction Dataset. NIPS 2016, workshop Future of Interactive Learning Machines, Dec 2016, Barcelona, Spain. 〈hal-01402479〉



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