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Found 10 papers, 5 papers with code
Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation
Finding controllers that perform well across multiple morphologies is an important milestone for large-scale robotics, in line with recent advances via foundation models in other areas of machine learning.
Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots
We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.
Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots
Soft robotics is a rapidly growing area of robotics research that would benefit greatly from design automation, given the challenges of manually engineering complex, compliant, and generally non-intuitive robot body plans and behaviors.
GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking
We show that our method is able to accurately learn a representation of the incorporated positive rank order, which is not only consistent with the ground truth but also proportional to the underlying information.
RLSEP: Learning Label Ranks for Multi-label Classification
Multi-label ranking maps instances to a ranked set of predicted labels from multiple possible classes.
Symmetry and Variance: Generative Parametric Modelling of Historical Brick Wall Patterns
This study integrates artificial intelligence and computational design tools to extract information from architectural heritage.
Single Image Depth Estimation: An Overview
We review solutions to the problem of depth estimation, arguably the most important subtask in scene understanding.
ODFNet: Using orientation distribution functions to characterize 3D point clouds
Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neural network architectures.
Ranked #18 on
3D Part Segmentation
on ShapeNet-Part
A New Distributional Ranking Loss With Uncertainty: Illustrated in Relative Depth Estimation
We propose a new approach for the problem of relative depth estimation from a single image.
Relative Depth Estimation as a Ranking Problem
To this end, we have introduced a listwise ranking loss borrowed from ranking literature, weighted ListMLE, to the relative depth estimation problem.
