We are near the end of a mad rush to get all the robotics equipment in our lab ready for the next sea trials. As always, no matter how much we think it will be a breeze, getting ready involves a lot of work and stress. Part of this is due to the constant pressure to push the bounds of what we can do and, once we get things stable, to add more complexity to the experiments being planned.
This year the part of the team from McGill is putting particular focus on three different classes of experiment: leaning-based robot guidance, optimal data collection given constraints on time, teleoperation and tele learning using Microsoft Robotics Studio, and enhanced controller design. See, I added a 4th experimental context just since the time I started this paragraph!
The learning-based guidance is based on work by my student Philippe Giguere where he uses a new learning rule to exploit the correlations is time that are present in most of the observations made in the real world. Using these temporal correlations, the process of learning about different classes of experience can be made easier. These classes of experience can be different terrain textures (as in our most recent RSS paper) where the robot learns how to adapt it's walking modes to the type of terrain it is on. For example, on slippery terrain a more "careful" low-speed gait is appropriate.
2009
2009
As part of our Sea Trials in Barbados, we held an Underwater Webcast in which we transmitted live image data and robot telemetry to a class at St. George's High School back in Montreal. We did this Jan 21st, 2009. This was accompanied by live streaming audio and video that I narrated from the beach. My colleague Ioannis Rekleitis operated the Microsoft Robotics Studio (MSRS) interface that was used to collect the telemetry data. Junaed Sattar sat in at the High School and gave an associated seminar that also served as a backup in case our Internet connection failed (it didn't, but it did have to be restarted a couple of times).
On the beach, in addition to providing narration for the experiment, I also did some question answering so it was a truly interactive webcast event. I also used the camera embedded in my laptop to provide a live video feed from the surface of what was going on. Thus, we had a webcast that involved live footage from both the surface and from underwater. To my knowledge, this is the first live interactive underwater webcast.
2009
How do you get the OpenCV vision package with Python bindings to compile on OS X, such that you can use a version of Python other than the one that is supplied with the original operating system installation? That's the problem I will discuss here. It's very hacky and not for casual reading, but my help some Googler.
The problem is that OpenCV insists on being linked with the standard default installation of Python on OS X. On the other hand, many (most?) techie people using Python will have installed an alternative newer release (for instance via "fink" or "macports").
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There's more. Read the whole story on "Opencv with Python under mac OS X "
