Adaptive local learning in sampling based motion planning for protein folding

Protein folding by motion planning.

We investigate a novel approach for studying protein folding that has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs). Our focus is to study issues related to the folding process, such as the formation of secondary and tertiary structures, assuming we know the native fold. A feature of our PRM-based framework is that the large sets of folding pathway...

Sampling-Based Motion Planning

Learning Sampling Distributions for Robot Motion Planning

A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to “small” regions of the state space, due to e.g. ...

Studying Protein Folding Using Motion Planning Techniques

The goal of this project is to use PRM (probabilistic roadmap) methods to study protein folding. Given a goal (native fold) configuration, we are able to construct a roadmap and derive a set of possible paths for the protein to follow. To do so, we model proteins as multi-link treelike robots with many degrees of freedom. Our work concentrates on improving our techniques for studying the potent...

Sampling-based algorithms for optimal motion planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a functio...