Shared Control for Human-Robot Cooperative Manipulation Tasks

In the past decade many studies on human motor control have investigated how humans are moving their arms. In robotics, these studies were usually used as a foundation for human-robot cooperation tasks. Nonetheless, the gap between human motor control and robot control remains challenging. In this paper we investigated, how human proprioceptive abilities could enhance performance of cooperative manipulative tasks, where humans and robots are autonomous agents coupled through physical interaction. In such setups, the robot movements are usually accurate but without the proprioceptive capabilities observed in humans. On the contrary, humans have well developed proprioceptive capabilities, but their movement accuracy is highly dependent on the speed of movement. In this paper we proposed an approach where we exploited the speed-accuracy trade-off model of a human together with the robotic partner. In this way the performance can be improved in a human-robot cooperative setup. The performance was analyzed on a task where a long object, i.e. a pipe, needs to be manipulated into a groove with different tolerances. We tested the accuracy and efficiency of performing the task. The results show that the proposed approach can successfully estimate human behavior and successfully perform the task.