Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
IEEE Intelligent Systems
Machine Learning
Machine Learning
RUR '95 Proceedings of the International Workshop on Reasoning with Uncertainty in Robotics
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Accelerating reinforcement learning through imitation
Accelerating reinforcement learning through imitation
Predicting human interruptibility with sensors
ACM Transactions on Computer-Human Interaction (TOCHI)
How may I serve you?: a robot companion approaching a seated person in a helping context
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Socially Distributed Perception: GRACE plays social tag at AAAI 2005
Autonomous Robots
Human-robot interaction: a survey
Foundations and Trends in Human-Computer Interaction
A semi-autonomous communication robot: a field trial at a train station
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Proactive learning: cost-sensitive active learning with multiple imperfect oracles
Proceedings of the 17th ACM conference on Information and knowledge management
Beyond usability evaluation: analysis of human-robot interaction at a major robotics competition
Human-Computer Interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Efficiently learning the accuracy of labeling sources for selective sampling
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Socially embedded learning of the office-conversant mobile robot Jijo-2
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Robots asking for directions: the willingness of passers-by to support robots
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Gracefully mitigating breakdowns in robotic services
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
An effective personal mobile robot agent through symbiotic human-robot interaction
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A model for types and levels of human interaction with automation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Shared understanding for collaborative control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Personalization in HRI: a longitudinal field experiment
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Human behavior understanding for robotics
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
| Hi-index | 0.00 |
Robots are increasingly autonomous in our environments, but they still must overcome limited sensing, reasoning, and actuating capabilities while completing services for humans. While some work has focused on robots that proactively request help from humans to reduce their limitations, the work often assumes that humans are supervising the robot and always available to help. In this work, we instead investigate the feasibility of asking for help from humans in the environment who benefit from its services. Unlike other human helpers that constantly monitor a robot's progress, humans in the environment are not supervisors and a robot must proactively navigate to them to receive help. We contribute a study that shows that several of our environment occupants are willing to help our robot, but, as expected, they have constraints that limit their availability due to their own work schedules. Interestingly, the study further shows that an available human is not always in close proximity to the robot. We present an extended model that includes the availability of humans in the environment, and demonstrate how a navigation planner can incorporate this information to plan paths that increase the likelihood that a robot can find an available helper when it needs one. Finally, we discuss further opportunities for the robot to adapt and learn from the occupants over time.