It’s so nice to have a productive week, even if you didn’t get any of the things you needed to do done. The final dissertation error analyses have been hanging over my head like the sword of damocles. They need to be done so that I can generate a decent fit of the path-integration errors for the models, but it’s soooooo boring.
Fortunately, a potential visit by the Discovery channel allowed me to redirect back to the robot. While making sure the monkey models still ran, I slipped in a quick communications test. I’ve been arguing since I got here that the synchronous, polling communications that we use to communicate with the robot and the various sensor processors was hideously inefficient. Understandably there was reluctance to go down the route of a communications retrofit. Most didn’t think it was a significant bottleneck, particularly since Lisp and Java are controlling the cognition (stereotyped views of the languages’ performance). I wasn’t sure how much time we were wasting, but a back of the envelope calculation convinced me that we could get at least a 30-50% improvement by going with a push model. So, while verifying the robot, I ran some networking benchmarks. It was worse than I’d thought. Much worse.
The maximum update rate I was able to achieve was around 2Hz. Single message transactions were taking well over 100ms, and each full update was taking an average of 5 transactions. I couldn’t believe it, so I double checked and triple checked. When I showed the numbers to our vision guy, he couldn’t believe it either. Fortunately, he had a mock client of his own. Sure enough, 2.5Hz. We agreed to work on a new scheme and test it out on his face tracker. The initial mock up is a subscription based, UDP mechanism where the client requests that data be delivered at a specific (and adaptable) rate. My tests have the system running in the kHz range with little system or network impact (small packets). Once the face tracker is ready, I think we’ll have enough evidence to merit a communications refit, finally allowing the robots perceptual systems to run at a more human rate of around 20Hz. There’s only one problem, Lisp doesn’t support UDP, but there is hope.
While I was verifying the model on the robot I was reminded of another concern I’d had. When we run demos, a significant amount of time is spent figuring out what has changed since the last time the demo worked. Software changes, as do the models. What we needed was a way to archive verified models and their dependencies. Lisp can generate self-contained executables (which we don’t seem to use too often).. and Eclipse certainly supports the same on the Java side. So I spent a day reverse engineering how Eclipse does it (a multi-step process that would be unrealistic to expect psychologists to deal with), simplified and customized for jACT-R models. It is now possible to export an executable model, its configuration and all dependencies as a single entity, making safe and repeatable demos that much more possible (knock on wood).
After that was completed I had one of those “A-ha!” moments. For a week or so I’ve been brainstorming on how to get jACT-R to perform a visual search along a line (to support gaze-following). I had actually implemented a few years ago, but that code had long since vanished (CVS->SVN migration). Sort candidate visual objects based on their distance from the line.. duh. I hate it when solutions are obvious and I’m just oblivious to them. Que sera. The code necessary to support this is actually extremely simple and should require only a minimum of fussing. Once that face tracker is up and running, we should be able to do some fairly plausible gaze-following.
Now if I could only muster the motivation to finish up those error analyses.
