Voice communication between humans and machines
Voice communication between humans and machines
Building a Multimodal Human-Robot Interface
IEEE Intelligent Systems
Linking Perception and Action in a Control Architecture for Human-Robot Domains
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 5 - Volume 5
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Teaching robots by moulding behavior and scaffolding the environment
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Exploiting referential context in spoken language interfaces for data-poor domains
Proceedings of the 13th international conference on Intelligent user interfaces
Teaching sequential tasks with repetition through demonstration
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
The snackbot: documenting the design of a robot for long-term human-robot interaction
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Mobile human-robot teaming with environmental tolerance
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Learning Visual Object Categories for Robot Affordance Prediction
International Journal of Robotics Research
Developing high-level cognitive functions for service robots
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Teaching a robot to perform tasks with voice commands
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Dynamic reward shaping: training a robot by voice
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Facilitating mental modeling in collaborative human-robot interaction through adverbial cues
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Tell me when and why to do it!: run-time planner model updates via natural language instruction
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
Toward learning perceptually grounded word meanings from unaligned parallel data
SIAC '12 Proceedings of the Second Workshop on Semantic Interpretation in an Actionable Context
Multi-mode Natural Language Processing for Extracting Open Knowledge
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Collaborative effort towards common ground in situated human-robot dialogue
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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Effective human/robot interfaces which mimic how humans interact with one another could ultimately lead to robots being accepted in a wider domain of applications. We present a framework for interactive task training of a mobile robot where the robot learns how to do various tasks while observing a human. In addition to observation, the robot listens to the human's speech and interprets the speech as behaviors that are required to be executed. This is especially important where individual steps of a given task may have contingencies that have to be dealt with depending on the situation. Finally, the context of the location where the task takes place and the people present factor heavily into the robot's interpretation of how to execute the task. In this paper, we describe the task training framework, describe how environmental context and communicative dialog with the human help the robot learn the task, and illustrate the utility of this approach with several experimental case studies.