图 书 馆
基于近似推理的机器人轨迹优化Robot Trajectory Optimization Using Approximate Inference |
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| 课程网址: | https://videolectures.net/videos/icml09_toussaint_rto |
| 主讲教师: | Marc Toussaint |
| 开课单位: | 会议 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to first compute an optimal (deterministic) trajectory and then solve a local linear-quadratic-gaussian (LQG) perturbation model to handle the system stochasticity. We present a new algorithm for this approach which improves upon previous algorithms like iLQG. We consider a probabilistic model for which the maximum likelihood (ML) trajectory coincides with the optimal trajectory and which, in the LQG case, reproduces the classical SOC solution. The algorithm then utilizes approximate inference methods (similar to expectation propagation) that efficiently generalize to non-LQG systems. We demonstrate the algorithm on a simulated 39-DoF humanoid robot. |
| 关 键 词: | 近似推理; 机器人; 轨迹优化 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-04-25：liyq |
| 最后编审: | 2025-04-25：liyq |
| 阅读次数: | 5 |