This is the video for "Efficient Motion Planning of Highly Articulated Chains using Physics-based Sampling," by Russell Gayle, Avneesh Sud, Stephane Redon, Ming C. Lin, and Dinesh Manocha.

In the first part of the video, an articulated chain with 300 degrees of freedom must navigate through a series of walls. We compare the performance of the fully articulated case of our approach with the version that has adaptively reduced dimensionality. 

The second part of the video shows the result for planning of a snake-like robot with 2000 degrees of freedom as it inspects some industrial piping. 