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Robot Learning & Interaction
The Robot Learning & Interaction group focuses on human-centric robot applications. The objective is to develop probabilistic approaches to encode movements and behaviors in robots evolving in unconstrained environments. In these applications, the models serve several purposes (recognition, prediction, online synthesis), and are shared by different learning strategies (imitation, emulation, incremental refinement or exploration). The aim is to facilitate the transfer of skills from end-users to robots, or in-between robots, by exploiting multimodal sensory information and by developing intuitive teaching interfaces.
The developed learning algorithms can be applied to a diverse robotic applications, with robots that are either close to us (assistive robots in I-DRESS), parts of us (prosthetic hands in TACT-HAND), or far away from us (manipulation skills in deep water with DexROV).
Current Group Members