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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
  • "We are investigating how to program robots so that they learn tasks from practice. One method, task-level learning, provides advantages over simply perfecting models of the robot's lower level systems. Task-level learning can compensate for the structural modeling errors of the robot's lower level control systems and can speed up the learning process by reducing the degrees of freedoms of the models to be learned. We demonstrate two general learning procedures-fixed-model learning and refined-model learning-on a ball-throwing robot system. Both learning approaches refine the task command based on the performance error of the system, while they ignore the intermediate variables separating the lower level systems. We provide both experimental and theoretical evidence that task-level learning can improve a robot's performance of a task."

http://schema.org/name

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