From Mai 2017 I am a PhD student supervised by Dr. Georg Martius. Before that, I finished my Master study in Medical Engineering & Mechanical Engineering in the RWTH Aachen University, and Bachelor study in Mechatronics at Chengxian Class of South East University. My research interests fall in the areas of haptic system, robotic system and medical imaging system. Currently, I am working on haptic projects aiming at designing machine learning driven full body sensing system for robots.
During the rapid development of robot technologies, actuators and sensors have become increasingly compact and powerful. Nevertheless, robots are still far from matching human capabilities especially when it comes to touch sensation. For this, haptic sensors have to be robust to sustain long-last...
Frontiers in Neurorobotics, 13, pages: 51, 2019 (article)
Robust haptic sensation systems are essential for obtaining dexterous robots. Currently, we
have solutions for small surface areas such as fingers, but affordable and robust techniques for
covering large areas of an arbitrary 3D surface are still missing. Here, we introduce a general
machine learning framework to infer multi-contact haptic forces on a 3D robot’s limb surface from
internal deformation measured by only a few physical sensors. The general idea of this framework
is to predict first the whole surface deformation pattern from the sparsely placed sensors and then
to infer number, locations and force magnitudes of unknown contact points. We show how this can
be done even if training data can only be obtained for single-contact points using transfer learning
at the example of a modified limb of the Poppy robot. With only 10 strain-gauge sensors we
obtain a high accuracy also for multiple-contact points. The method can be applied to arbitrarily
shaped surfaces and physical sensor types, as long as training data can be obtained.
Proceedings International Conference on Humanoid Robots, pages: 846-853, IEEE, New York, NY, USA, 2018 IEEE-RAS International Conference on Humanoid Robots, 2018, Oral Presentation (conference)
Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot  and obtain 8 mm localization precision.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems