In this paper we present a novel robotic palpation method for the lump shape estimation using contact pressure distribution. Many previous researches about the robotic palpation have used a stiffness map, which is not suitable to obtain geometrical information of a lump. As a result, they require a large data set and long palpation time to estimate the lump shape. Instead of using the stiffness map, the proposed palpation method uses the difference between the normal force direction and the surface normal to detect the lump boundary and estimate its normal. The palpation trajectory is generated by the normal of the lump boundary to track the lump boundary in real-time. The proposed approach requires small data set and short palpation time for the lump shape estimation since the shape can be directly estimated from the optimally generated palpation trajectory. An experiment result shows that our method can find the lump shape accurately in real-time with small data and short time.

We introduce a learning system for the sight reading of simple drum sequences. Sight reading is a cognitive-motor skill that requires reading of music symbols and actions of multiple limbs for playing the music. The system provides knowledge of results (KR) pertaining to the learner's performance by color-coding music symbols, and guides the learner by indicating the corresponding action for a given music symbol using additional auditory or vibrotactile cues. To evaluate the effects of KR and guidance cues, three learning methods were experimentally compared: KR only, KR with auditory cues, and KR with vibrotactile cues. The task was to play a random 16-note-long drum sequence displayed on a screen. Thirty university students learned the task using one of the learning methods in a between-subjects design. The experimental results did not show statistically significant differences between the methods in terms of task accuracy and completion time.

We present the initial results of on-going research for building a novel Mobile Haptic Interface (MHI) that can provide an unlimited haptic workspace in large immersive virtual environments. When a user explores a large virtual environment, the MHI can sense the position and orientation of the user, place itself to an appropriate configuration, and deliver force feedback, thereby enabling a virtually limitless workspace. Our MHI (PoMHI v0.5) features with omnidirectional mobility, a collision-free motion planning algorithm, and force feedback for general environment models. We also provide experimental results that show the fidelity of our mobile haptic interface.