13 papers
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…
Investigating Adaptive Tuning of Assistive Exoskeletons Using Offline Reinforcement Learning: Challenges and Insights
Yasin Findik, Christopher Coco, Reza Azadeh
Assistive exoskeletons have shown great potential in enhancing mobility for individuals with motor impairments, yet their effectiveness relies on precise parameter tuning for perso…
Advances in Multi-agent Reinforcement Learning: Persistent Autonomy and Robot Learning Lab Report 2024
Reza Azadeh
Multi-Agent Reinforcement Learning (MARL) approaches have emerged as popular solutions to address the general challenges of cooperation in multi-agent environments, where the succe…
Relational Weight Optimization for Enhancing Team Performance in Multi-Agent Multi-Armed Bandits
Monish Reddy Kotturu, Saniya Vahedian Movahed, Paul Robinette +3
We introduce an approach to improve team performance in a Multi-Agent Multi-Armed Bandit (MAMAB) framework using Fastest Mixing Markov Chain (FMMC) and Fastest Distributed Linear A…
Graph Attention Inference of Network Topology in Multi-Agent Systems
Akshay Kolli, Reza Azadeh, Kshitj Jerath
Accurately identifying the underlying graph structures of multi-agent systems remains a difficult challenge. Our work introduces a novel machine learning-based solution that levera…
Collaborative Adaptation for Recovery from Unforeseen Malfunctions in Discrete and Continuous MARL Domains
Yasin Findik, Hunter Hasenfus, Reza Azadeh
Cooperative multi-agent learning plays a crucial role for developing effective strategies to achieve individual or shared objectives in multi-agent teams. In real-world settings, a…
Relational Q-Functionals: Multi-Agent Learning to Recover from Unforeseen Robot Malfunctions in Continuous Action Domains
Yasin Findik, Paul Robinette, Kshitij Jerath +1
Cooperative multi-agent learning methods are essential in developing effective cooperation strategies in multi-agent domains. In robotics, these methods extend beyond multi-robot s…
Investigating the Generalizability of Assistive Robots Models over Various Tasks
Hamid Osooli, Christopher Coco, Johnathan Spanos +2
In the domain of assistive robotics, the significance of effective modeling is well acknowledged. Prior research has primarily focused on enhancing model accuracy or involved the c…
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…
Design of Fuzzy Logic Parameter Tuners for Upper-Limb Assistive Robots
Christopher Coco, Jonathan Spanos, Hamid Osooli +1
Assistive Exoskeleton Robots are helping restore functions to people suffering from underlying medical conditions. These robots require precise tuning of hyper-parameters to feel n…
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…