Improving robot manipulation with data-driven object-centric models of everyday forces

Robots currently lack common sense about the forces involved in everyday manipulation tasks. In this paper, we present data-driven methods to inform robots about the forces that they are likely to encounter when performing specific tasks. In the context of door opening, we demonstrate that data-driven models can be used to haptically recognize specific doors, haptically recognize classes of door (e.g., refrigerator vs. kitchen cabinet), and better detect haptic events (e.g., a locked door or collision), even when opening a specific door for the first time. We also show that two distinct robots can use forces captured from people opening doors to better detect haptic events. This illustrates the potential for databases of task-specific forces to be captured by people and robots in order to inform robot manipulation.