图 书 馆
近似推理控制Approximate Inference Control |
|
| 课程网址: | http://videolectures.net/nipsworkshops09_toussaint_aic/ |
| 主讲教师: | Marc Toussaint |
| 开课单位: | 柏林工业大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Approximate Inference Control (AICO) is a method for solving Stochastic Optimal Control (SOC) problems. The general idea is to think of control as the problem of computing a posterior over trajectories and control signals conditioned on constraints and goals. Since exact inference is infeasible in realistic scenarios, the key for high-speed planning and control algorithms is the choice of approximations. In this talk I will introduce to the general approach, discuss its intimate relations to DDP and the current research on Kalman's duality, and discuss the approximations that we use to get towards real-time planning in high-dimensional robotic systems. I will also mention recent work on using Expectation Propagation and truncated Gaussians for inference under hard constraints and limits as they typically arise in robotics (collision and joint limit constraints). |
| 关 键 词: | 优化方法 ; 机器学习; 计算机科学; 机器人 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-03：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 33 |