However, in the motion recognition phase, the motion is classified into its specific model, and in the motion generation phase, a command specifying the model is given to a robot.
We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives.
By modeling the low-dimensional sequential features, we can achieve motion recognition, which is confirmed by experiments.
More specifically, indices of the motion models that are not understood by human partners intervene in the motion recognition and generation.
For testing the recognition model, the kitchen was equipped with sensors of different types: tactile floor sensors, motion detectors, door contact sensors, window contacts, light barriers, temperature sensors, a humidity sensor, brightness sensors, a microphone, and a camera.
However, all observers identified the Upright stimulus as a human model in postural imbalance indicating that kinematic information, rather than the visual context, is tuning motion recognition [22].
The motion symbols allow motion recognition and synthesis.
We have developed an imitative learning framework based on the symbolization of motion patterns using Hidden Markov Models (HMMs), where each HMM (hereafter referred to as "motion symbol") abstracts the dynamics of a motion pattern and allows motion recognition and generation.
Existing human motion recognition techniques are applied to recognise the human motions.
An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots.
