Contact force estimation from flexible tactile sensor values considering hysteresis by Gaussian process
Takato Horii, Francesco Giovannini, Yukie Nagai, Lorenzo Natale, Giorgio Metta, Minoru Asada
- 发表年份
- 2014
- 引用次数
- 2
摘要
Flexible tactile sensors are important elements for facilitating the physical interaction between robots and uncertain environments. For instance, tactile information is used by the robot to grasp objects and interact with humans. A model-based approach is one technique for building a relationship between tactile sensor values and task-relevant information such as force, slip, and temperature. However, it is difficult to create models of flexible tactile sensors for converting sensor signals beforehand due to a nonlinear relation between a contact and the deformations of the flexible form caused by its hysteresis [1]. In contrast, machine learning techniques can be adopted to represent these relationships. For example, Tada et al. [2] proposed a model to acquire the relationship between tactile sensor values and slip vibration using a neural network.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002