In this paper, we present a method for two robot manipulators to learn cooperative assembly tasks. A learning algorithm based on trial end error is used to find a sequence for each robot to assemble the goal aggregate. It is shown that a distributed learning method based on a Markov decision process is able to learn the sequences for the involved robots. A novel state-action graph is used to store the reinforcement values of the learning process. The approach is designed in a way that not only exact matches but also similar aggregates are accepted by the system.

We propose a concept for integrating multiple sensors in real-time robot control. To increase the controller robustness under diverse uncertainties, the robot systematically generates series of sensor data (as robot state) while memorising the corresponding motion parameters. From the collection of (multi-) sensor trajectories, statistical indices for each sensor type can be extracted. If the sensor data are preselected as output relevant, the principal components can be used very efficiently to approximately represent the original perception scenarios. After this dimension reduction procedure, a nonlinear fuzzy controller can be trained to map the subspace projection into the robot control parameters. We apply the approach to a real robot system with two arms and multiple vision and force/torque sensors. These external sensors are used simultaneously to control the robot arm performing insertion and screwing operations. The experiments show that the robustness as well as the precision of robot control can be enhanced by integrating multiple additional sensors using this concept.

Human-beings can easily describe their behaviour by IF-THEN rules, which can be transferred from one task to another with slight local changes. However, standard techniques for function approximation like neural networks or associative memories are unable to work with rules. We introduce a method for extracting and importing human readable rules from and to a B-spline fuzzy controller. Rule import is used to initialise a B-spline fuzzy controller with a priori knowledge to decrease the learning time and overcome the problem of partially trained B-spline controllers. In the experimental section we show how a set of rules for a two arm cooperation task are generated through "learning-by-doing" and transferred to a robot screwing operation. The successful experiment shows how rule-based knowledge can be used for skill transfer in similar tasks.

To enable a two-arm manipulator system to perform complex cooperating tasks, such as carrying a rigid object, establishing a contact to a surface, grinding, etc., a nonlinear multiple input system should be solved. We apply an approach to online automatic learning of a B-spline fuzzy controller. This controller model directly connects the sensor inputs to the compensation motion. By using the adaptation of control actions in all possible situations through practising of the robots in the real environment, uncertainties of the robot-object model can be taken into account. The compliant motion controller of the robot system can be adapted to new situations in a short time due to the online learning approach.