ML92 Proceedings of the ninth international workshop on Machine learning
The robot in the garden: telerobotics and telepistemology in the age of the Internet
The robot in the garden: telerobotics and telepistemology in the age of the Internet
Robust visualization for online control of mobile robots
Beyond webcams
Machine Learning
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Imitation in animals and artifacts
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Confidence-based policy learning from demonstration using Gaussian mixture models
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A survey of robot learning from demonstration
Robotics and Autonomous Systems
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Toward an ecological approach to interface design for teaching robots
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
ROS and Rosbridge: roboticists out of the loop
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Evaluating the effects of limited perception on interactive decisions in mixed robotic domains
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
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We present a study of using a robotic learning from demonstration system capable of collecting large amounts of human-robot interaction data through a web-based interface. We examine the effect of different perceptual mappings between the human teacher and robot on the learning from demonstration. We show that humans are significantly more effective at teaching a robot to navigate a maze when presented with information that is limited to the robot's perception of the world, even though their task performance measurably suffers when contrasted with users provided with a natural and detailed raw video feed. Robots trained on such demonstrations learn more quickly, perform more accurately and generalize better. We also demonstrate a set of software tools for enabling internet-mediated human-robot interaction and gathering the large datasets that such crowdsourcing makes possible.