Anticipatory Planning for Human-Robot Teams

When robots work alongside humans for performing collaborative tasks, they need to be able to anticipate human's future actions and plan appropriate actions. The tasks we consider are performed in contextually-rich environments containing objects, and there is a large variation in the way humans perform these tasks. We use a graphical model to represent the state-space, where we model the humans through their low-level kinematics as well as their high-level intent, and model their interactions with the objects through physically-grounded object affordances. This allows our model to anticipate a belief about possible future human actions, and we model the human's and robot's behavior through an MDP in this rich state-space. We further discuss that due to perception errors and the limitations of the model, the human may not take the optimal action and therefore we present robot's anticipatory planning with different behaviors of the human within the model's scope. In experiments on Cornell Activity Dataset, we show that our method performs better than various baselines for collaborative planning.