A User-Adaptive Gesture Recognition System Applied to Human-Robot Collaboration in Factories

Enabling Human-Robot collaboration (HRC) requires robot with the capacity to understand its environment and actions performed by persons interacting with it. In this paper we are dealing with industrial collaborative robots on assembly line in automotive factories. These robots have to work with operators on common tasks. We are working on technical gestures recognition to allow robot to understand which task is being executed by the operator, in order to synchronize its actions. We are using a depth-camera with a top view and we track hands positions of the worker. We use discrete HMMs to learn and recognize technical gestures. We are also interested in a system of gestures recognition which can adapt itself to the operator. Indeed, a same technical gesture seems very similar from an operator to another, but each operator has his/her own way to perform it. In this paper, we study an adaptation of the recognition system by modifying the learning database with a addition very small amount of gestures. Our research shows that by adding 2 sets of gestures to be recognized from the operator who is working with the robot, which represents less than 1% of the database, we can improve correct recognitions rate by similar to 3.5%. When we add 10 sets of gestures, 2.6% of the database, the improvement reaches 5.7%.