5 papers · 1 filter
One-Shot Multimodal Learning from Demonstration with Force-Constrained Elastic Maps
Brendan Hertel, Jonathan Spanos, Navya Garg +1
Robotic manipulation tasks often require simultaneous reasoning over motion and contact forces, yet most Learning from Demonstration (LfD) methods model only spatial trajectories a…
Parameter-Free Segmentation of Robot Movements with Cross-Correlation Using Different Similarity Metrics
Wendy Carvalho, Meriem Elkoudi, Brendan Hertel +1
Often, robots are asked to execute primitive movements, whether as a single action or in a series of actions representing a larger, more complex task. These movements can be learne…
Robot Learning Using Multi-Coordinate Elastic Maps
Brendan Hertel, Reza Azadeh
To learn manipulation skills, robots need to understand the features of those skills. An easy way for robots to learn is through Learning from Demonstration (LfD), where the robot…
A Framework for Learning and Reusing Robotic Skills
Brendan Hertel, Nhu Tran, Meriem Elkoudi +1
In this paper, we present our work in progress towards creating a library of motion primitives. This library facilitates easier and more intuitive learning and reusing of robotic s…
An Adaptive Framework for Manipulator Skill Reproduction in Dynamic Environments
Ryan Donald, Brendan Hertel, Stephen Misenti +2
Robot skill learning and execution in uncertain and dynamic environments is a challenging task. This paper proposes an adaptive framework that combines Learning from Demonstration…