As robots are introduced into human environments
\nfor long periods of time, human owners and collaborators will
\nexpect them to remember shared events that occur during execution.
\nBeyond naturalness of having memories about recent and
\nlonger-term engagements with people, such execution memories
\ncan be important in tasks that persist over time by allowing
\nrobots to ground their dialog and to refer efficiently to previous
\nevents. In this work, we define execution memory as the capability
\nof saving interaction event information and recalling it for later
\nuse. We divide the problem into four parts: salience filtering of
\nsensor evidence and saving to short term memory, archiving from
\nshort to long term memory and caching from long to short term
\nmemory, and recalling memories for use in state inference and
\npolicy execution. We then provide examples of how execution
\nmemory can be used to enhance user experience with robots.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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