Stefanos Nikolaidis

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We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task. We first present a computational formulation of the robot's interrole knowledge and(More)
We present a framework for automatically learning human user models from joint-action demonstrations that enables a robot to compute a robust policy for a collaborative task with a human. First, the demonstrated action sequences are clustered into different human types using an unsupervised learning algorithm. A reward function is then learned for each type(More)
In this paper, we propose an object localization method for home environments. This method utilizes RFID equipments, a mobile robot and some ceiling cameras. The RFID system estimates a rough position of each object. The autonomous robot with two modules of RFID antenna explores the environment to detect other objects on the floor. Each object, attached by(More)
—Human-robot collaboration presents an opportunity to improve the efficiency of manufacturing and assembly processes , particularly for aerospace manufacturing where tight integration and variability in the build process make physical isolation of robotic-only work challenging. In this paper, we develop a robotic scheduling and control capability that(More)
New industrial robotic systems that operate in the same physical space as people highlight the emerging need for robots that can integrate seamlessly into human group dynamics. In this paper we build on our prior investigation, which evaluates the convergence of a robot computational teaming model and a human teammate's mental model, by computing the(More)
In the near future robots will be used in home environments to provide assistance for the elderly and challenged people. The arrangement of sensors influences greatly the quality of information provided to the robot. We, therefore, examine the problem of the optimal arrangement of vision sensors for the case of a robot following a pre-defined path. A(More)
—We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using(More)
We design and evaluate a method of human–robot cross-training, a validated and widely used strategy for the effective training of human teams. Cross-training is an interactive planning method in which team members iteratively switch roles with one another to learn a shared plan for the performance of a collaborative task. We first present a computational(More)