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缩小机器与人之间的差距

Bridging the gap between machines and people
课程网址: http://videolectures.net/snnsymposium2010_roy_bgbm/  
主讲教师: Nicholas Roy
开课单位: 麻省理工学院
开课时间: 2010-12-01
课程语种: 英语
中文简介:
在过去几年中,机器人在世界上的运作方式取得了很大进展。例子包括DARPA大挑战和城市挑战中的自动驾驶车辆,机器人绘图方面的大量工作,以及对家庭和服务机器人的日益增长的兴趣。然而,这些示例技术和系统仍然主要限于研究原型。获得更广泛使用的机器人的一个障碍是,机器人对他们的世界的推理方式仍然与人们的推理方式截然不同。机器人在点特征,密集占用网格和行动成本图方面进​​行思考。人们根据地标,分段对象和任务(以及其他表示)来思考。有很好的理由说明这些是不同的,机器人不可能以与人们相同的方式推理世界。但是,最近的工作是弥合低级几何和控制之间的差距,以及更高级别的语义表示。我将讨论如何使用机器学习来开发能够在人口稠密的环境中运行并执行复杂任务的更强大的机器人。我将讨论最先进的技术,开放的挑战以及解决这些挑战的潜在影响。
课程简介: In the last few years, how robots operate in the world has advanced considerably. Examples include the autonomous vehicles in the DARPA Grand Challenges and Urban Challenge, the considerable work in robot mapping, and the growing interest in home and service robots. However, these example technologies and systems are still mostly restricted to research prototypes. One obstacle to getting more widely useful robots is that the way robots reason about their world is still pretty different to how people reason. Robots think in terms of point features, dense occupancy grids and action cost maps. People think in terms of landmarks, segmented objects and tasks (among other representations). There are good reasons why these are different, and robots are unlikely to ever reason about the world in the same way that people do. But, there has been recent work in bridging the gap between low-level geometry and control, and higher-level semantic representations. I will talk about how machine learning is being used to develop more capable robots that can operate in populated environments and perform complex tasks. I will discuss the state of the art, what the open challenges are and the potential impact of solving these challenges.
关 键 词: 机器人定位; 家庭服务机器人; 机器人
课程来源: 视频讲座网
最后编审: 2020-10-01:yumf
阅读次数: 67