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http://worldcat.org/entity/work/id/21384354

Task-level robot learning : ball throwing

We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot's performance of a wide range of dynamic tasks. We have developed {\it task-level learning} that successfully improves a robot's performance of two complex tasks, ball-throwing and juggling. With task-level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. This learning method serves to complement other approaches, such as model calibration, for improving robot performance.

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http://schema.org/alternateName

  • "AI-TR-1079"
  • "Robot learning, Task-level"@en

http://schema.org/description

  • "We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot's performance of a wide range of dynamic tasks. Our interest is in complex tasks such as throwing, catching, batting, yo-yoing, and juggling. We have developed one method of learning, task-level learning, that successfully improves a robot's performance of both a ball-throwing and a juggling task. With task-level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. For example, we have programmed a robot to juggle a single ball in three dimensions. The robot practices the juggling task by batting a ball into the air with a large paddle. The robot uses a real-time binary vision system to track the ball and measure its own performance. Task-level learning consists of building a model of the performance errors at the task level during practice. The robot compensates for the performance errors by using that model to refine the task-level commands. When using task-level learning, the number of hits that the robot can execute before the ball is hit out of range dramatically improves."
  • "We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot's performance of a wide range of dynamic tasks. We have developed {\it task-level learning} that successfully improves a robot's performance of two complex tasks, ball-throwing and juggling. With task-level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. This learning method serves to complement other approaches, such as model calibration, for improving robot performance."@en

http://schema.org/name

  • "Task-Level Robot Learning"
  • "Task-level robot learning : Ball throwing"
  • "Task-level robot learning : ball throwing"@en
  • "Task-level robot learning"@en
  • "Task-level robot learning"