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Human-Robot Collaboration in industrial context requires a smooth, natural and efficient coordination between robot and human operators. The approach we propose to achieve this goal is to use online recognition of technical gestures. In this paper, we present together, and analyze, parameterize and evaluate much more thoroughly, three findings previously unveiled separately by us in several conference presentations: 1/ we show on a real prototype that multi-users continuous real-time recognition of technical gestures on an assembly-line is feasible (≈ 90% recall and precision in our case-study), using only non-intrusive sensors (depth-camera with a top-view, plus inertial sensors placed on tools); 2/ we formulate an end-to-end methodology for designing and developing such a system; 3/ we propose a method for adapting to new users our gesture recognition. Furthermore we present here two new findings: 1/ by comparing recognition performances using several sets of features, we highlight the importance of choosing features that focus on the effective part of gestures, i.e. usually hands movements; 2/ we obtain new results suggesting that enriching a multiusers training set can lead to higher precision than using a separate training dataset for each operator.

International audienceEnabling 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 ~3.5%. When we add 10 sets of gestures, 2.6% of the database, the improvement reaches 5.7%