The third Workshop on
Reconstruction of Human-Object Interactions (RHOBIN)

Learning Versatile Humanoid-Scene Interaction Skills from Human Motion

Equipping humanoid robots with interactive capabilities across a wide range of scenarios is a central objective in embodied artificial intelligence. However, the process of skill acquisition in humanoid robots is challenging due to their complex dynamics, high-dimensional perception and control demands, and underactuated nature. Fortunately, the morphological similarity between humanoid robots and humans offers a unique advantage: the vast repository of human interaction motion data serves as a valuable source of prior knowledge. This talk focuses on how to efficiently utilize this data to develop diverse interaction skills in humanoid robots. I will present three ways to leverage human motion data: learning through trial and error with human motion priors, learning by tracking human interactions, and learning by interacting with digital humans. These methods highlight the transformative potential of human motion data in advancing humanoid skills.

Presentations of the RHOBIN Challenge
results given by the winners

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