Wearable Inertial Measurement Units (IMUs) allow nonintrusive motion tracking, but limited sensor placements can introduce uncertainty in capturing detailed full-body movements. We introduce the Probabilistic Inertial Poser (ProbIP), a novel probabilistic model that transforms sparse IMU Read more…
Despite the prevalence of robotic manipulation tasks in various real-world applications of different requirements and needs, there has been a lack of focus on enhancing the adaptability of robotic grasping systems. Most of the current Read more…
This work proposes newly an incremental motion learning algorithm through kinesthetic teachings and a new motion production algorithm by combining learned motions in a humanoid robot. The proposed algorithms are useful to enrich producible motions Read more…