Implicitly Assisting Humans to Choose Good Grasps in Robot to Human Handovers

We focus on selecting handover configurations that result in low human ergonomic cost not only at the time of handover, but also when the human is achieving a goal with the object after that handover. People take objects using whatever grasping configuration is most comfortable to them. When the human has a goal pose they’d like to place the object at, however, the most comfortable grasping configuration at the handover might be cumbersome overall, requiring regrasping or the use of an uncomfortable configuration to reach the goal. We enable robots to purposefully influence the choices available to the person when taking the object, implicitly helping the person avoid suboptimal solutions and account for the goal. We introduce a probabilistic model of how humans select grasping configurations, and use this model to optimize expected cost. We present results in simulation, as well as from a user study, showing that the robot successfully influences people’s grasping configurations for the better.