This chapter describes a novel dual-arm motion discrimination method that combines posterior probabilities estimated independently for left and right arm movements, and its application to control a robotic manipulator. The proposed method estimates the posterior probability of each single-arm motion through learning using recurrent probabilistic neural networks. The posterior probabilities output from the networks are then combined based on motion dependency between arms, making it possible to calculate a joint posterior probability of dual-arm motions. With this method, all the dual-arm motions consisting of each single-arm motion can be discriminated through leaning of single-arm motions only. In the experiments performed, the proposed method was applied to the discrimination of up to 50 dual-arm motions. The results showed that the method enables relatively high discrimination performance. In addition, the possibility of applying the proposed method for a human-robot interface was confirmed through operation experiments for the robotic manipulator using dual-arm motions.
Discrimination of Dual-arm Motions Using a Joint Posterior Probability Neural Network