Human-robot interactions (HRI) have been recognized to be a key element of future robots in manufacturing and transportation, which entail huge social and economical impacts. Technically, it is challenging to design the behavior of these robots, as they need to operate in highly unstructured and stochastic environments. The fundamental problem to address in this talk is how to ensure these robots operate efficiently and safely in human-involved dynamic uncertain environments. The microscopic aspect of the problem, i.e. the design of the behavior system for single robot through learning, motion planning and control in the framework of adaptive optimal control, will be discussed first. A novel real-time non-convex optimization solver that handles nonlinear robot dynamics and non-convex safety constraints will be introduced to ensure timely responses of the robot during interactions. Then the macroscopic aspect of the problem, i.e. the analysis and synthesis of the human-robot system from a game theoretical multi-agent system perspective, will be discussed. The performance of the method will be illustrated through human-in-the-loop experiments on industrial collaborative robot manipulators and automated vehicles. These works demonstrate a promising way to build intelligent robots that can better serve, assist and collaborate with people in our daily lives across work, home and leisure.
Speaker: Changliu Liu, Stanford
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