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australiannetting · 19 days

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softrobotcritics · 1 year

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"Liquid" neural networks in robots

(...)

TechCrunch: I get the sense that one of the motivating factors [in the creation of liquid neural networks] are hardware constraints with mobile robotic systems and their inability to run extremely complex computations and rely on these very complex neural networks.

Daniela Rus: That is part of it, but most importantly, the reason we started thinking about liquid networks has to do with some of the limitations of today’s AI systems, which prevent them from being very effective for safety, critical systems and robotics. Most of the robotics applications are safety critical.

I assume most of the people who have been working on neural networks would like to do more with less if that were an option.

Yes, but it’s not just about doing more with less. There are several ways in which liquid networks can help robotic applications. From a computational point of view, the liquid networkers are much smaller. They are compact, and they can run on a Raspberry Pi and EDGE devices. They don’t need the cloud. They can run on the kinds of hardware platforms that we have in robotics.

We get other benefits. They’re related to whether you can understand how the machine learning system makes decisions — or not. You can understand whether the decision is going to meet some safety constraints or not. With a huge machine learning model, it is actually impossible to know how that model makes decisions. The machine learning models that are based on deep networks are really huge and opaque.

And unpredictable.

They are opaque and unpredictable. This presents a challenge, as users are unable to comprehend how decisions are made by these systems. This is really a problem if you want to know if your robot will move correctly — whether the robot will respond correctly to a perceptual input. The first fatal accident for Tesla was due to a machine learning imperfection. It was due to a perception error. The car was riding on the highway, there was a white truck that was crossing and the perceptual system believed that the white truck was a cloud. It was a mistake. It was not the kind of mistake that a human would make. It’s the kind of mistake that a machine would make. In safety critical systems, we cannot afford to have those kinds of mistakes.

Having a more compact system, where we can show that the system doesn’t hallucinate, where we can show that the system is causal and where we can maybe extract a decision tree that presents in understandable human form how the system reaches a conclusion, is an advantage. These are properties that we have for liquid networks. Another thing we can do with liquid networks is we can take a model and wrap it in something we call “BarrierNet.” That means adding another layer to the machine learning engine that incorporates the constraints that you would have in a control barrier function formulation for control theoretic solutions for robots. What we get from BarrierNet is a guarantee that the system will be stable and fall within some bounds of safety.

You’re talking about intentionally adding more constraints.

Yes, and they are the kind of constraints that we see in control theoretic, model predictive control solutions for robots. There is a body of work that is developing something called “control barrier functions.” It’s this idea that you have a function that forces the output of a system within a safe region. You could take a machine learning system, wrap it with BarrierNet, and that filters and ensures that the output will fall within the safety limits of the system. For example, BarrierNet can be used to make sure your robot will not exert a force or a torque outside its range of capability. If you have a robot that aims to apply force that is bigger than what it can do, then the robot will break itself. There are examples of what we can do with BarrieNet and Liquid Networks. Liquid networks are excitig because they address issues that we have with some of today’s systems.

They use less data and less training time. They’re smaller, and they use much less inference time. They are provably causal. They have action that is much more focused on the task than other machine learning approaches. They make decisions by somehow understanding aspects of the task, rather than the context of the task. For some applications, the context is very important, so having a solution that relies on context is important. But for many robotic applications, the context is not what matters. What matters is how you actually do the task. If you’ve seen my video, it shows how a deep network would keep a car on the road by looking at bushes and trees on the side of the road. Whereas the liquid networks have a different way of deciding what to do. In the context of driving, that means looking at the road’s horizon and the side of the road. This is, in some sense, a benefit, because it allows us to capture more about the task.

It’s avoiding distraction. It’s avoiding being focused on every aspect of the environment.

Exactly. It’s much more focused.

Do these systems require different kinds and amounts of training data than more traditional networks?

In our experiments, we have trained all the systems using the same data, because we want to compare them with respect to performance after training them in the same way. There are ways to reduce the data that is needed to train a system, and we don’t know yet whether these data reduction techniques can be applied broadly — in other words, they can benefit all models. Or if they are particularly useful for a model that is formulated in the dynamical systems formulation that we have for liquid networks.

I want to add that liquid neural networks require time series data. A liquid network solution does not work on datasets consisting of static data items. We cannot use ImageNet with liquid neural networks right now. We have some ideas, but as of now, one really needs an application with time series to use liquid networks.

The methods for reducing the amount of information that we feed into our models are somehow related to this notion that there’s a lot of repetition. So not all the data items give you effective new information. There is data that brings and widens information diversity, and then there’s data that’s more of the same. For our solutions, we have found that if we can curate the datasets so that they have diversity and less repetition, then that is the fine way to move forward.

Ramin [Hasani] gave a TEDx talk at MIT. One of the things he said, which makes a lot of sense on the face of it is, “bad data means bad performance.”

Garbage in, garbage out.....

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australiannetting · 2 months

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australiannetting · 2 months

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australiannetting · 2 months

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australiannetting · 4 months

Text

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australiannetting · 2 months

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