What I was wondering was how could a spacial map be realized, with neural components or programmatically? It's obvious that working modern control systems in nature use a dynamic neural model/representation of the environment that is updated and synched with own movements. Any ideas?
In neural systems there are millions of things happening in parallel, all interacting with each other, it is impossible to emulate that computationally.
The software can only simulate a static neural system with fixed connections. Nothing that can't be more effectively done with imperative programming - using the available tech, not one we wish we had. But it's useful for research. Just my 2 cents on that.
Spacial mapping is one of the first things I hope to work on once I add visual systems to the simulation and robotics kit. In particular, I plan to start by expanding on work done by others on ant navigation. A good reference for this is here (Ralf, M. (1999). Insect visual homing strategies in a robot with analog processing, 1–15.). This is based on the average landmark vector (ALV) theory of insect navigation. Basically, the idea is that an ant's visual system finds vertical and horizontal lines within a very wide scene area where it is located, and processes these in a way that produces a unique set of values that characterizes the current location of the insect. You can compare two ALV vectors to get an idea of where you are on a terrain. They did some really neat experiments using simple edge detection to allow the robot to navigate around the environment. You can then tie this into a reward system and a dead-reckoning system to begin forming value-based terrain maps. I plan to implement this for the AnimatLab robotics framework and start expanding on this work. My hope is to get it to the point where a robot can begin exploring its environment to form a visual mental map of where things are relative to its current location (battery charger, room to clean, toy box, stairs, etc..) The Moller lab has done a lot of work in this area that I would like to reproduce and start using.
AnimatLab is primarily focused on neural systems. That is its basic niche. It was originally designed for doing neuromechanical simulations of living organisms. I always planned on enhancing it for use in robotics. It has just taken longer to get there than I had hoped. However, even there I still see AnimatLab's primary focus being on biomimetic (biologically inspired) control systems. I do have plans to add the ability to use other types of control types though. For example, I would like to add the ability to add RobotC or scripting languages like python as a codeblock that can interact with the other components. So you could still layout the connections of various motors/sensors graphically and then add a codeblock that would open an editor/debugger to implement the actual control of those devices. Different codeblocks could be combined/re-used and they could still be connected to neural components as well. This would let the designer have more choices as to how he wanted to implement each section. There may be some stuff where it just makes more sense to use pure code and other parts where you want neural systems that can learn, or others may not have any desire to use neural components at all. It should be up to the designer to decide how he wants to implement the various sections of the control system. Also, even now you can implement C++ control nodes directly using the SDK and easily plug them into your robot. I am just starting on the robotics add-ons for AnimatLab, so I have not had time to add most of this yet, but I agree there needs to be other means of controlling the robot besides neural systems. This is something that I will be adding at some point in the near future.
At the moment, you are correct that it can often take a lot of manual tuning of the NN to get it to behave the way you want. A big part of this is the fact that learning has not really been pursued within AnimatLab to the level that will be needed in the future. When we can simulate very large NNs on a Tegra or desktop GPU it will not be possible to do this type of fine-tuning by hand. From my perspective, one of the main goals of the biomimetic approach is to emulate the incredible adaptability of real animals. A wildebeest or an ant can walk and interact with its environment within a few moments of being born because evolution has helped do some initial setup of its neural net, and then it rapidly learns the tweaks needed to get up and going. Robots should behave the same way. Biomemetic NNs should use optimization techniques and learning to allow a robot to rapidly learn how to move, and how to adapt to changes like damage or loss of a limb. As soon as I get some more of the basics setup for robotitcs in AnimatLab, this type of learning is something I plan to work on a lot.
I understand your concerns about why you feel the use of NNs is the wrong approach. It is a lot simpler to just setup a basic algorithm to control walking gaits or to switch between behavioral modes. However, I believe that most people who are interested in the biomimetic approach feel that our best bet for getting truly intelligent and adaptive robots is by studying and attempting to duplicate how real, working systems operate and learn. So while it may seem like it is inefficient and unnecessary, I personally believe this this is the path that will ultimately lead to those types of breakthroughs in robotics. That being said, I would also like to grow AnimatLab into something that anyone can use to simulate and build robots regardless of the approach they want to take.
Hope that helps, and thanks for your feedback.
@dcofer That's a lot of projects. Keep up the good work.
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