Learning about objects with human teachers
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Dialog in the open world: platform and applications
Proceedings of the 2009 international conference on Multimodal interfaces
Transparent active learning for robots
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Jogger: models for context-sensitive reminding
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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As robots are introduced into human environments for long periods of time, human owners and collaborators will expect them to remember shared events that occur during execution. Beyond naturalness of having memories about recent and longer-term engagements with people, such execution memories can be important in tasks that persist over time by allowing robots to ground their dialog and to refer efficiently to previous events. In this work, we define execution memory as the capability of saving interaction event information and recalling it for later use. We divide the problem into four parts: salience filtering of sensor evidence and saving to short term memory, archiving from short to long term memory and caching from long to short term memory, and recalling memories for use in state inference and policy execution. We then provide examples of how execution memory can be used to enhance user experience with robots.