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…