5 accepted papers
We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in humanrobot collaborative tasks. To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction to anticipate the human's motion and adapt the robot's jo…
As robot autonomy improves, robots are increasingly being considered in the role of autonomous observation systems - free-flying cameras capable of actively tracking human activity within some predefined area of interest. In this work, we formulate the autonomous observation problem through multi-ob…
We address the problem of learning Hierarchical Task Networks (HTNs) from unannotated task demonstrations, while retaining action execution preferences present in the demonstration data. We show that the problem of learning a complex HTN structure can be made analogous to the problem of series/paral
Autonomous grasp selection for robot pick-and-place applications makes use of either empirical methods leveraging object databases, which generate grasps for specific objects at the initial cost of modeling effort, or analytical methods, which generalize to novel objects but fail on object subsets t…
In order for robots to be useful in real world learning scenarios, non-expert human teachers must be able to interact with and teach robots in an intuitive manner. One essential robot capability is wide-area (mobile or nonstationary) pick-and-place tasks. Even in its simplest form, pick-and-place is…