Search Results for author:
Found 10 papers, 7 papers with code
Bayesian Floor Field: Transferring people flow predictions across environments
Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model is only able to describe the dynamics of the environment it has been built in.
DDGC: Generative Deep Dexterous Grasping in Clutter
Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps.
Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition.
Notes on the Behavior of MC Dropout
Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness.
Beyond Top-Grasps Through Scene Completion
Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps.
Robust Grasp Planning Over Uncertain Shape Completions
We present a method for planning robust grasps over uncertain shape completed objects.
Robotics
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation.
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring.
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation.
