Synthesizing Object Receiving Motions of Humanoid Robots with Human Motion Database
Katsu Yamane (Disney Research Pittsburgh)
Marcel Revfi (Karlsruhe Institute of Technology)
Tamim Asfour (Karlsruhe Institute of Technology)
This project presents a method for synthesizing motions of a humanoid robot that receives an object from a human, with focus on a natural object passing scenario where the human initiates the passing motion by moving an object toward the robot, which continuously adapts its motion to the observed human motion in realtime. In this scenario, the robot not only has to recognize and adapt to the human action but also has to synthesize its motion quickly so that the human does not have to wait holding an object. We solve these issues by using a human motion database obtained from two persons performing the object passing task. The rationale behind this approach is that human performance of such a simple task is repeatable, and therefore the receiver (robot) motion can be synthesized by looking up the passer motion in a database. We demonstrate in simulation that the robot can start extending the arm at an appropriate timing and take hand conﬁgurations suitable for the object being passed. We also perform hardware experiments of object handing from a human to a robot.