Mimesis model from partial observations for a humanoid robot

This paper proposes a new mimesis scheme for partial observations, consisting of two strategies; (1) motion understanding from partial observations and (2) proto-symbol-based motion duplication. With the proposed method, whole-body motion imitation is possible even when observing partial motion data. The scheme enables a humanoid robot to imitate a new observed motion by utilizing its own prior knowledge, without learning the demonstrated motion. Evaluation factors, such as inheritance coordinate and matching error, are introduced to evaluate imitation performance. The feasibility of the proposed scheme is demonstrated by an evaluation for a 20-degree-of-freedom humanoid robot.