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Learning Behavior Trees From Demonstration
2019 International Conference on Robotics and Automation (ICRA), 2019Robotic Learning from Demonstration (LfD) allows anyone, not just experts, to program a robot for an arbitrary task. Many LfD methods focus on low level primitive actions such as manipulator trajectories. Complex multistep task with many primitive actions must be learned from demonstration if LfD is to encompass the full range of task a user may desire.
Kevin French +4 more
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A survey of robot learning from demonstration
Robotics and Autonomous Systems, 2009We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research ...
Sonia Chernova +2 more
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Learning grasping force from demonstration
2012 IEEE International Conference on Robotics and Automation, 2012This paper presents a novel force learning framework to learn fingertip force for a grasping and manipulation process from a human teacher with a force imaging approach. A demonstration station is designed to measure fingertip force without attaching force sensor on fingertips or objects so that this approach can be used with daily living objects.
Yun Lin 0003 +3 more
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Learning from demonstration with swarm hierarchies
International Joint Conference on Autonomous Agents and Multiagent Systems, 2012We present a supervised learning from demonstration system capable of training stateful and recurrent collective behaviors for multiple agents or robots. A model space of this kind is often high-dimensional and consequently may require a large number of samples to learn.
Keith Sullivan, Sean Luke
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Policy transformation for learning from demonstration
Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, 2012Many different robot learning from demonstration methods have been applied and tested in various environments recently. Representation of learned plans, tasks and policies often depends on the technique due to method-specific parameters. An agent that is able to switch between representations can apply its knowledge to different algorithms.
Halit Bener Suay, Sonia Chernova
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Robot learning from demonstration
Robotics and Autonomous Systems, 2004Aude Billard, Roland Siegwart
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Robot Learning to Paint from Demonstrations
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022Younghyo Park +2 more
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Recent Advances in Robot Learning from Demonstration
Annual Review of Control, Robotics, and Autonomous Systems, 2020Sonia Chernova +2 more
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Continual learning from demonstration of robotics skills
Robotics and Autonomous Systems, 2023Jakob Johannes Hollenstein +2 more
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