This is a page for uploading papers and short snippets to summarize the paper and sketch out ideas. Presently, it will be used by Pulkit and Mayur to explore ideas for representing 3D worlds.
Discussion on June 30 A general algorithm for transforming signals from vision to motor domain. The simple test case: Sparse coding on spatio-temporal data as a robot moves around with the world. The robot has gyros/accelerometers (referred as motion readings) attached to it. We wish to establish correlation between some of the learnt dictionary elements and the motion readings. For example a dictionary element might be a vertical bar moving from center to the periphery which would be positively correlated with forward motion. The things to investigate are: 1. Difference in the learnt dictionary elements with and without motion cues. (One simple way of incorporating motion cues is to treat motion as an extra dimension). For example we might not get dictionary elements which directly correlate with forward/backward motion unless we put in motion cues. It will be interesting to investigate such things. 2. Next, when a video is shown to the agent - it should be able to infer forward or backward motion.