Absent-minded Robots Remember What Matters

We are constantly inundated with new information, and to manage it effectively it’s sometimes necessary to forget old, irrelevant memories.

Researchers at Vanderbilt University have now developed an algorithm that mimics this kind of forgetfulness in robots, as a way to filter out less useful information.

“Forgetting is a critical capability when operating in dynamic environments,” says PhD student Sanford Freedman, who presented the group’s data filtering-software, called ActSimple, in a paper published at the IASTED Robotics and Applications conference held this week in Cambridge, MA.

ActSimple draws on two facets of human memory: time-based decay, or the way that memories disappear over time, and interference, which is the failure to recall information due to other memories competing for attention. ActSimple assigns different pieces of data values depending on how often they are used, and how similar it is to other pieces of information.

To test the software, the researchers used it to control a simulated robot that measured the strength of WiFi signals in a virtual environment. The robot recorded WiFi readings on a scale of 1-100, as it moved through the virtual setting and these WiFi readings also had different levels of noise (errors) associated with them. At intervals, the robot relied on its memory to create an estimated WiFi signal map by recalling signal strength information it had gathered and stored. The researchers tested ActSimple against four other algorithms, including one that strictly disregarded the oldest information, and another that out filtered random information.

The Team found that on average, ActSimple created the most reliable estimated WiFi map. Interestingly, when the robot “remembered” everything–that is, used all of its gathered information (errors and all)–it generated the least accurate map overall.