Improving Manipulation Capabilities of Autonomous Robots

A. Vetro
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Abstract

Human-level manipulation continues to be beyond the capabilities of today’s robotic systems. Not only do current industrial robots require significant time to program a specific task, but they lack the flexibility to generalize to other tasks and be robust to changes in the environment. While collaborative robots help to reduce programming effort and improve the user interface, they still fall short on generalization and robustness. This talk will highlight recent advances in a number of key areas to improve the manipulation capabilities of autonomous robots, including methods to accurately model the dynamics of the robot and contact forces, sensors and signal processing algorithms to provide improved perception, optimization-based decision-making and control techniques, as well as new methods of interactivity to accelerate and enhance robot learning.
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改进自主机器人的操作能力
人类水平的操作仍然超出了当今机器人系统的能力。目前的工业机器人不仅需要大量的时间来编程一个特定的任务,而且它们缺乏推广到其他任务的灵活性和对环境变化的鲁棒性。虽然协作机器人有助于减少编程工作并改善用户界面,但它们在泛化和鲁棒性方面仍然不足。本次演讲将重点介绍一些关键领域的最新进展,以提高自主机器人的操作能力,包括精确模拟机器人动力学和接触力的方法,传感器和信号处理算法,以提供改进的感知,基于优化的决策和控制技术,以及加速和增强机器人学习的新交互性方法。
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