The usual ways to plot a course from one place to another typically entail a trade-off between economy of motion and computation time. Researchers instead used deep learning to find efficient paths quickly.
What’s new: The researchers propose a recurrent neural network that generates good motion plans in a fixed time regardless of the complexity of the environment. This video gives an overview.
Why it’s better: Their system’s performance scales almost linearly with higher dimensions, outperforming algorithms like A* and RRT* that bog down in larger or more complex spaces. It proved 10 times faster with a three-link robot, 20 times faster with four links, and 30 times faster controlling a three link arm in six dimensions.
How it works: Known as OracleNet, the model mimics the stepwise output of an oracle algorithm (one that can generate efficient paths to or from any spot in a given space). At each waypoint, the network uses the current position and the goal location to decide on the position of the next waypoint. It uses an LSTM to preserve information over several steps.
Why it matters: Robotic control is devilishly hard, and typical solutions impose severe limitations. This research offers an inkling of what neural networks can accomplish in the field.
What’s next: Researchers Mayur Bency, Ahmed Qureshi, and Michael Yip suggest that active learning could enable OracleNet to accommodate dymnamic environments. Further variations could enable it to navigate unfamiliar or changing environments.