用触觉感知反馈主动序贯估计目标动力学Active Sequential Estimation of Object Dynamics with Tactile Sensory Feedback |
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课程网址: | http://videolectures.net/rss2010_ting_ase/ |
主讲教师: | Jo-Anne Ting |
开课单位: | 不列颠哥伦比亚大学 |
开课时间: | 2010-11-08 |
课程语种: | 英语 |
中文简介: | 估计物体动力学的参数,例如粘度或内部自由度,是物体的自主和灵巧机器人操纵的关键。通常,由于复杂的高度非线性基础物理过程,准确且有效地估计这些对象参数可能是具有挑战性的。为了提高手工制作解决方案的质量,我们研究了如何自动生成控制策略。我们提出了一个主动学习框架,它按顺序收集数据样本,使用信息理论标准来找到在每个时间步骤执行的最佳操作。我们的框架在机器人手臂上进行评估,其中任务涉及优化动作(摇动频率和摇动的旋转)以确定液体的粘度,仅给出触觉感觉反馈。活动框架比其他简单策略表现更好,并加快了估算的收敛速度。 |
课程简介: | Estimating parameters of object dynamics, such as viscosity or internal degrees of freedom, is key in the autonomous and dexterous robotic manipulation of objects. Oftentimes, it may be challenging to accurately and efficiently estimate these object parameters due to the complex highly nonlinear underlying physical processes. In an effort to improve the quality of hand-crafted solutions, we examine how control strategies can be automatically generated. We present an active learning framework that sequentially gathers data samples, using information-theoretic criteria to find the optimal actions to perform at each time step. Our framework is evaluated on a robotic hand-arm where the task involves optimizing actions (shaking frequency and rotation of shaking) in order determine viscosity of liquids, given only tactile sensory feedback. The active framework performs better than other simple strategies and speeds up the convergence of estimates. |
关 键 词: | 物体动力学; 主动学习框架; 机器人 |
课程来源: | 视频讲座网 |
最后编审: | 2019-09-17:lxf |
阅读次数: | 58 |