Current Mood: 
Blogs I Commented On:
Rant:
I like having assigned readings for Monday's class, because we have five days to read over them. Assigned readings for Wednesday's class? Eh...not so zesty.
Anyway, looks like Brandon's once again the only person caught up with his blog posts for the course. He so h4x like a 1337 h4x0r. L4m3.
Summary:
The authors of this paper focus on a system where a robot learns a task by observing a teacher. The downfall of existing systems is the lack of mechanism for online teaching of gestures with symbolic meanings. This paper claims a gesture recognition system which can interactively learn gestures with as few as 1-2 examples. Their approach is with HMMs, and their device is a Cyberglove for the domain of sign language. Their general procedure for interactive training was: 1) user makes a gesture, 2) system segments input into separate gesture for classification (if it’s certain of the classification, then perform the action on it; else query the user for confirmation), 3) system adds symbols of that gesture to the list, then updates the HMM based on the example.
For the gesture recognition, the system preprocesses the raw data into a sequence of discrete observation symbols, determines which set of HMMs most likely generated that sequence, and checks if there is ambiguity between two or more gestures or if there is no known gesture similar to the observed data. For learning gesture models, the Baum-Welch (BW) algorithm was used to find the HMM that is a local maximum in the likelihood of generated observed sequences. To allow for an online, interactive style of gesture training, the HMMs were trained by beginning with one or some small number of examples, BW was run until it converges, and the system iteratively adds more examples while updating the model with BW on each one. In the signal preprocessing stage, the use of discrete HMMs required representing gestures as a sequence of discrete symbols, where the hand was treated as a single entity, and the sequence was generated as a single-dimensional sequence of features representing the entire hand. The preprocessing algorithm of the input data was a vector quantization of a series of short-time fast Fourier transforms.
The implementation of the hand gesture recognition system used 5-state Bakis HMMs, restricting the system to only move from a given state to the same state or one of the next two states. This allowed for the assumption of simple and non-cyclical sequence of motions. Classification errors yielded 1.0% and 2.4% after two examples, 0.1% after four examples, and none after six examples.
Discussion:
This paper fits pretty well in the context of this course. It’s cited by the Iba paper on gesture-based control for mobile robots, it focuses on the domain of sign language like the Allen paper, and resorts heavily on HMMs as introduced in the Rabiner paper. Even though I wasn’t too familiar with the Baum-Welch algorithm, a key aspect in their implementation, I liked how they modified its use for training HMMs from an offline, batch-based approach to an interactive and online-based approach. Unfortunately, they did not give the results for how its performance compared against the typical batch-based approach, only making note that their online training approach came close to the results of an offline one.
1 comments:
the online training aspect was interesting but like Aaron pointed out on his blog, ideally you would want to use gestures to interact with robots so you don't have to use a keyboard and mouse, but in order to train interactively you have to have some means of telling the robot what the new gesture means.
Post a Comment