It’s been a while since I last updated this blog. My excuse is that I’ve been busy but who isn’t? ;) Just submitted a couple of conference papers with another journal paper in the review stage—I’ll post them up here soon. I’m excited about one of the conference papers which ties complex networks metrics with machine learning (specifically, centrality with a sparse GP), which can have applications in a broad range of areas.
Working at SMART has been quite enjoyable this past year; from hanging out in the (autonomous vehicle) garage to data analytics @ the fishbowl (pictures to come). A few of my fellow postdocs have moved on to greener pastures since I joined, which has gotten me thinking about what lies ahead. As usual, there is some internal tension brewing between choosing academia or industry. I’m tend towards the former but am open to the latter—given the limited number of tenure-track faculty positions available, I believe this to be the best perspective. Anyone with experience have any thoughts?
I’ll update more often from now on given I’ve mostly settled down into a routine. Oh, and I’m engaged so, a wedding next year. Crazy, exciting time in my life!
Well, my first working day at SMART is almost over. 4 minutes and 35 seconds to the stipulated end-of-work-day. But who’s counting? So far, it’s been interesting — met the friendly folks here and saw the cool toys (autonomous vehicles). I’m one of the “early birds” and managed to land a desk with a great view of the NUS campus. That said, I might move down to “The Garage” where all the robots/machines are. Hopefully, I’ll sort out all my administration stuff soon and get on to
playing working with the vehicles and some new learning methods I have in mind.
It’s been a while since my last post. Excuse: thesis write-up. Update: Thesis submitted!
In other news, our recent work on Learning Assistance by Demonstration was accepted this year’s IROS! It’ll be a fun and interesting conference in Tokyo, Japan! You can find a preprint here.
Abstract: Crafting a proper assistance policy is a difficult endeavour but essential for the development of robotic assistants. Indeed, assistance is a complex issue that depends not only on the task-at-hand, but also on the state of the user, environment and competing objectives. As a way forward, this paper proposes learning the task of assistance through observation; an approach we term Learning Assistance by Demonstration (LAD). Our methodology is a subclass of Learning-by-Demonstration (LbD), yet directly addresses difficult issues associated with proper assistance such as when and how to appropriately assist. To learn assistive policies, we develop a probabilistic model that explicitly captures these elements and provide efficient, online, training methods. Experimental results on smart mobility assistance — using both simulation and a real-world smart wheelchair platform — demonstrate the effectiveness of our approach; the LAD model quickly learns when to assist (achieving an AUC score of 0.95 after only one demonstration) and improves with additional examples. Results show that this translates into better task-performance; our LAD-enabled smart wheelchair improved participant driving performance (measured in lap seconds) by 20.6s (a speedup of 137%), after a single teacher demonstration.
Download Draft PDF
Going back in time…
This Wired article on the Internet Archive reminded me of a talk I once attended (at UCD) by the founder, Brewster Kahle. In addition to being technologically impressive, the Archive’s WayBackMachine is tremendously fun. Try visiting Google back in 1999.
Most PhD students know that at some point, the dreaded PhD Avoidance Syndrome sets in. A few whatsapp and real-world conversations led to this:
Slightly Longer Version: http://www.youtube.com/watch?v=rTX9WcgjZqM
YARP is another robot development platform, similar to ROS. I had to code up a simple data reader in Python (operating over YARP ports) and couldn’t find any good examples. After some experimenting, I found a solution that worked for me. The following is a simple code snippet for other YARP Python newbies:
#create a new input port and open it
self.in_port = yarp.BufferedPortBottle()
#connect up the output port to our input port
#in this example, I assume the data is a single integer
#we use read() where the parameter determines if it is
#blocking (True) or not.
btl = self.in_port.read(True)
my_data = btl.get(0).asInt()
#if you have doubles, you can use asDouble()
#or strings can be obtained using asString()
I did the unthinkable and upgraded my OS (in my final year of my PhD!). And surprise-surprise, some of my code wouldn’t compile anymore. I figured I needed to rebuild my macports-installed *nix software but ran into problems with gcc45 and libstdcxx. The issue is a ld64 bug, that was fixed using user adrian’s solution (replicated here):
sudo port uninstall ld64
sudo port -v install ld64
sudo port clean libstdcxx
sudo port -d build libstdcxx build.jobs=1
sudo port install libstdcxx