Motion Planning for Human-Robot Collaborative Manipulation Tasks Using Prediction of Human Motion

In this paper we present a framework that allows a human and a robot to perform simultaneous manipulation tasks safely in close proximity. The proposed framework is based on early prediction of the human’s motion. The prediction system, which builds on previous work in the area of gesture recognition, generates a prediction of human workspace occupancy by computing the swept volume of learned human motion trajectories. The motion planner then plans robot trajectories that minimize a penetration cost in the human workspace occupancy while interleaving planning and execution. We test our framework in simulation using recorded human motions and a simulated PR2 robot. We show that taking into account the predicted human workspace occupancy in the robot’s motion planner leads to safer and more efficient interactions between the user and the robot than only considering the human’s current configuration. The paper concludes by discussing discussing future directions to enhance the current framework.