Search Results for author:
Found 4 papers, 2 papers with code
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space.
cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex Polytope
The algorithm is suitable for the fast registration of markers in augmentedreality systems and in applications where a computationally efficient real time feature detector is necessary. The algorithm can also be extended to N-dimensional polyhedrons.
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries.
Using Local Experiences for Global Motion Planning
In this work, we decompose the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and store them in a database.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
