3 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
Learning Dense Visual Descriptors using Image Augmentations for Robot Manipulation Tasks
Christian Graf, David B. Adrian, Joshua Weil +5
We propose a self-supervised training approach for learning view-invariant dense visual descriptors using image augmentations. Unlike existing works, which often require complex da…
Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints
Akshay Dhonthi, Philipp Schillinger, Leonel Rozo +1
For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories…
Flexible Behavior Trees: In search of the mythical HFSMBTH for Collaborative Autonomy in Robotics
Joshua M. Zutell, David C. Conner, Philipp Schillinger
In recent years, the model of computation known as Behavior Trees (BT), first developed in the video game industry, has become more popular in the robotics community for defining d…
Study of Signal Temporal Logic Robustness Metrics for Robotic Tasks Optimization
Akshay Dhonthi, Philipp Schillinger, Leonel Rozo +1
Signal Temporal Logic (STL) is an efficient technique for describing temporal constraints. It can play a significant role in robotic manipulation, for example, to optimize the robo…
Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications
Andras Kupcsik, Markus Spies, Alexander Klein +4
Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced dense object descriptors as a novel visual object representation for the robotics community. It is suit…
Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
Leonel Rozo, Meng Guo, Andras G. Kupcsik +8
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current works…