Robot locomotion is an active research area. In this paper we focus on the locomotion of quadruped robots. An effective walking gait of quadruped robots is mainly concerned with two key aspects, namely speed and stabi...Robot locomotion is an active research area. In this paper we focus on the locomotion of quadruped robots. An effective walking gait of quadruped robots is mainly concerned with two key aspects, namely speed and stability. The large search space of potential parameter settings for leg joints means that hand tuning is not feasible in general. As a result walking parameters are typically determined using machine learning techniques. A major shortcoming of using machine learning techniques is the significant wear and tear of robots since many parameter combinations need to be evaluated before an optimal solution is found. This paper proposes a direct walking gait learning approach, which is specifically designed to reduce wear and tear of robot motors, joints and other hardware. In essence we provide an effective learning mechanism that leads to a solution in a faster convergence time than previous algorithms. The results demonstrate that the new learning algorithm obtains a faster convergence to the best solutions in a short run. This approach is significant in obtaining faster walking gaits which will be useful for a wide range of applications where speed and stability are important. Future work will extend our methods so that the faster convergence algorithm can be applied to a two legged humanoid and lead to less wear and tear whilst still developing a fast and stable gait.展开更多
概念漂移是数据流挖掘中不可避免的难点问题,其典型特征是数据分布随时间可能发生改变.针对现有模型处理数据流分类任务时出现过拟合的问题,本文提出了一种目标解耦驱动的在线深度网络(Online Deep Network driven by Target Decoupling...概念漂移是数据流挖掘中不可避免的难点问题,其典型特征是数据分布随时间可能发生改变.针对现有模型处理数据流分类任务时出现过拟合的问题,本文提出了一种目标解耦驱动的在线深度网络(Online Deep Network driven by Target Decoupling,ODNTD).首先,该模型从历史数据流中学习一个任务未知型特征提取器,实现了对任务的无偏见表示学习,从而增强了模型的泛化能力;其次,模型利用任务特定的权重调整,使得任务未知的通用特征表示能够适应具体任务,通过这种目标任务的权重学习进一步提升了模型的适应性.实验结果表明,所提出的方法对含概念漂移的数据流有良好的泛化性能.展开更多
文摘Robot locomotion is an active research area. In this paper we focus on the locomotion of quadruped robots. An effective walking gait of quadruped robots is mainly concerned with two key aspects, namely speed and stability. The large search space of potential parameter settings for leg joints means that hand tuning is not feasible in general. As a result walking parameters are typically determined using machine learning techniques. A major shortcoming of using machine learning techniques is the significant wear and tear of robots since many parameter combinations need to be evaluated before an optimal solution is found. This paper proposes a direct walking gait learning approach, which is specifically designed to reduce wear and tear of robot motors, joints and other hardware. In essence we provide an effective learning mechanism that leads to a solution in a faster convergence time than previous algorithms. The results demonstrate that the new learning algorithm obtains a faster convergence to the best solutions in a short run. This approach is significant in obtaining faster walking gaits which will be useful for a wide range of applications where speed and stability are important. Future work will extend our methods so that the faster convergence algorithm can be applied to a two legged humanoid and lead to less wear and tear whilst still developing a fast and stable gait.
文摘概念漂移是数据流挖掘中不可避免的难点问题,其典型特征是数据分布随时间可能发生改变.针对现有模型处理数据流分类任务时出现过拟合的问题,本文提出了一种目标解耦驱动的在线深度网络(Online Deep Network driven by Target Decoupling,ODNTD).首先,该模型从历史数据流中学习一个任务未知型特征提取器,实现了对任务的无偏见表示学习,从而增强了模型的泛化能力;其次,模型利用任务特定的权重调整,使得任务未知的通用特征表示能够适应具体任务,通过这种目标任务的权重学习进一步提升了模型的适应性.实验结果表明,所提出的方法对含概念漂移的数据流有良好的泛化性能.
