Bidirectional association of joint angle trajectories for humanoid locomotion: the restricted Boltzmann machine approach

Humans walk is the combination of different discrete subphases. The joint trajectories (ankle, hip, and knee) are different in every instance for human walking. So the association of these trajectories is difficult for stable walking. This research presents a novel approach to associate the joint trajectories of humanoid walking for every instance and different speeds. It is the first attempt to extract the features from the joint angle trajectories with the help of a restricted Boltzmann machine. The extracted features of joint trajectories are associated with bidirectional associative memory. The proposed approach has several advantages, since it is possible to associate the most appropriate trajectory when the input trajectory is not present in the supervised data. The BAM provides the most associative significant trajectories nearest to the input trajectory. This novel approach is well suited for real-time scenarios of stable walking of humanoid robots. By providing only one sensory data (trajectory) at any of joints among hip, knee, and ankle, the another trajectories can be associated that is most important application of this research. The indigenous data set has been generated for the experiments, and the results are verified with real-time and simulation environment of NaO and HOAP2 biped robots.