A retro-projected face display system has great advantages in being able to present realistic 3D appearances to users and to easily switch the appearance of the humanoid robot heads animated on the display. Therefore, it is useful to evaluate how effectively users can perceive various information from such devices and what type of animation is suitable for human-robot interaction - in particular, face-to-face communication with robots. In this paper, we examine how facial texture images affect people's ability to identify the gender of faces displayed on a retro-projected face screen system known as Mask-bot. In an evaluation study, we use a female face screen as the 3D output surface, and display various face images morphed between male and female. Subjects are asked to rate the gender of each projected face. We found that even though the output 3D mask screen has a female shape, gender identification is strongly determined by texture images, especially in the case of high-quality images.

Here we describe a parameter-driven solution for generating novel yet similar movements from a sparse example set obtained through observation. In our experiments, a humanoid learns to represent movement trajectories demonstrated by a person with intuitive parameters describing the start and end points of different motion trajectory segments. These segments are automatically produced based on changes in curvature. After rebinning to equate similar segments across the samples, we use a linear approximation framework to build a representation based on relevant task features (segment start and end points) where radial basis functions(RBFs) are used to approximate the unknown non-linear characteristics describing a trajectory. The solution is accomplished on-line and requires no interaction. With this approach a humanoid can learn from only a few examples, and quickly produce new movements.

We seek intuitive, efficient ways to create and direct human-like behaviors for humanoid robots. Here we present a method to enable humanoid robots to acquire movements by imitation. The robot uses 3D vision to perceive the movements of a human teacher, and then estimates the teacher's body postures using a fast full-body inverse kinematics method that incorporates a kinematic model of the teacher. This solution is then mapped to the robot and reproduced in real-time. The robustness of the method is tested on a 30-degree-of-freedom Sarcos humanoid robot located at ATR using 3D vision data from external cameras and from head-mounted cameras.

Using the pre-recorded human motion and trajectory tracking, we can control the motion of a humanoid robot for free-space, upper body gestures. However, the number of degrees of freedom, range of joint motion, and achievable joint velocities of today's humanoid robots are far more limited than those of the average human subject. In this paper, we explore a set of techniques for limiting human motion of upper body gestures to that achievable by a Sarcos humanoid robot located at ATR. We assess the quality of the results by comparing the motion of the human actor to that of the robot, both visually and quantitatively.

The formulation and optimization of joint trajectories for humanoid robots is quite different from this same task for standard robots because of the complexity of the humanoid robots' kinematics. We exploit the similarity between the movements of a humanoid robot and human movements to generate joint trajectories for such robots. In particular we show how to transform human motion information captured by an optical tracking device into a high dimensional trajectory of a humanoid robot. We utilize B-spline wavelets to efficiently represent the joint trajectories and to automatically select the density of the basis functions on the time axis. We applied our method to the task of teaching a humanoid robot how to make a dance movement.