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—We describe the Open Motion Planning Library (OMPL), a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new(More)
— We present a new approach to path planning for deformable linear (one-dimensional) objects such as flexible wires. We introduce a method for efficiently computing stable configurations of a wire subject to manipulation constraints. These configurations correspond to minimal-energy curves. By restricting the planner to minimal-energy curves, the execution(More)
The definition of reaction coordinates for the characterization of a protein-folding reaction has long been a controversial issue, even for the "simple" case in which one single free-energy barrier separates the folded and unfolded ensemble. We propose a general approach to this problem to obtain a few collective coordinates by using nonlinear(More)
– Self-reconfigurable robots are modular robots that can autonomously change their shape and size to meet specific operational demands. Recently, there has been a great interest in using self-reconfigurable robots in applications such as reconnaissance, rescue missions, and space applications. Designing and controlling self-reconfigurable robots is a(More)
Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, temporary storage in the hippocampus, and a slow, long-term storage within the neocortex. This paper presents a neural(More)
When multiple robots operate in the same environment, it is desirable for scalability purposes to coordinate their motion in a distributed fashion while providing guarantees about their safety. If the robots have to respect second-order dynamics, this becomes a challenging problem, even for static environments. This work presents a replanning framework(More)
BACKGROUND There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function(More)
— Partially-Observable Markov Decision Processes (POMDPs) are a problem class with significant applicability to robotics when considering the uncertainty present in the real world, however, they quickly become intractable for large state and action spaces. A method to create a less complex but accurate action model approximation is proposed and evaluated(More)