19 citations · 62 across the 17 of their papers we have counts for
18 papers
Mixed Q-Functionals: Advancing Value-Based Methods in Cooperative MARL with Continuous Action Domains
Yasin Findik, S. Reza Ahmadzadeh
Tackling multi-agent learning problems efficiently is a challenging task in continuous action domains. While value-based algorithms excel in sample efficiency when applied to discr…
Impact of Relational Networks in Multi-Agent Learning: A Value-Based Factorization View
Yasin Findik, Paul Robinette, Kshitij Jerath +1
Effective coordination and cooperation among agents are crucial for accomplishing individual or shared objectives in multi-agent systems. In many real-world multi-agent systems, ag…
Influence of Team Interactions on Multi-Robot Cooperation: A Relational Network Perspective
Yasin Findik, Hamid Osooli, Paul Robinette +2
Relational networks within a team play a critical role in the performance of many real-world multi-robot systems. To successfully accomplish tasks that require cooperation and coor…
Collaborative Adaptation: Learning to Recover from Unforeseen Malfunctions in Multi-Robot Teams
Yasin Findik, Paul Robinette, Kshitij Jerath +1
Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared…
Research Report -- Persistent Autonomy and Robot Learning Lab
S. Reza Ahmadzadeh
Robots capable of performing manipulation tasks in a broad range of missions in unstructured environments can develop numerous applications to impact and enhance human life. Existi…
Confidence-Based Skill Reproduction Through Perturbation Analysis
Brendan Hertel, S. Reza Ahmadzadeh
Several methods exist for teaching robots, with one of the most prominent being Learning from Demonstration (LfD). Many LfD representations can be formulated as constrained optimiz…
Design and Evaluation of a Bioinspired Tendon-Driven 3D-Printed Robotic Eye with Active Vision Capabilities
Hamid Osooli, Mohsen Irani Rahaghi, S. Reza Ahmadzadeh
The field of robotics has seen significant advancements in recent years, particularly in the development of humanoid robots. One area of research that has yet to be fully explored…
Contextual Autonomy Evaluation of Unmanned Aerial Vehicles in Subterranean Environments
Ryan Donald, Peter Gavriel, Adam Norton +1
In this paper we focus on the evaluation of contextual autonomy for robots. More specifically, we propose a fuzzy framework for calculating the autonomy score for a small Unmanned…
Robot Learning from Demonstration Using Elastic Maps
Brendan Hertel, Matthew Pelland, S. Reza Ahmadzadeh
Learning from Demonstration (LfD) is a popular method of reproducing and generalizing robot skills from human-provided demonstrations. In this paper, we propose a novel optimizatio…
Methods for Combining and Representing Non-Contextual Autonomy Scores for Unmanned Aerial Systems
Brendan Hertel, Ryan Donald, Christian Dumas +1
Measuring an overall autonomy score for a robotic system requires the combination of a set of relevant aspects and features of the system that might be measured in different units,…
Moral-Trust Violation vs Performance-Trust Violation by a Robot: Which Hurts More?
Zahra Rezaei Khavas, Russell Perkins, S. Reza Ahmadzadeh +1
In recent years a modern conceptualization of trust in human-robot interaction (HRI) was introduced by Ullman et al.\cite{ullman2018does}. This new conceptualization of trust sugge…
Similarity-Aware Skill Reproduction based on Multi-Representational Learning from Demonstration
Brendan Hertel, S. Reza Ahmadzadeh
Learning from Demonstration (LfD) algorithms enable humans to teach new skills to robots through demonstrations. The learned skills can be robustly reproduced from the identical or…
Learning from Successful and Failed Demonstrations via Optimization
Brendan Hertel, S. Reza Ahmadzadeh
Learning from Demonstration (LfD) is a popular approach that allows humans to teach robots new skills by showing the correct way(s) of performing the desired skill. Human-provided…
Towards Mobile Multi-Task Manipulation in a Confined and Integrated Environment with Irregular Objects
Zhao Han, Jordan Allspaw, Gregory LeMasurier +4
The FetchIt! Mobile Manipulation Challenge, held at the IEEE International Conference on Robots and Automation (ICRA) in May 2019, offered an environment with complex and integrate…
Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance
M. Asif Rana, Daphne Chen, S. Reza Ahmadzadeh +3
In this work, we contribute a large-scale study benchmarking the performance of multiple motion-based learning from demonstration approaches. Given the number and diversity of exis…
Skill Acquisition via Automated Multi-Coordinate Cost Balancing
Harish Ravichandar, S. Reza Ahmadzadeh, M. Asif Rana +1
We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes…
Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
Muhammad Asif Rana, Mustafa Mukadam, Seyed Reza Ahmadzadeh +2
Learning from Demonstration (LfD) is a popular approach to endowing robots with skills without having to program them by hand. Typically, LfD relies on human demonstrations in clut…
Visuospatial Skill Learning for Robots
S. Reza Ahmadzadeh, Fulvio Mastrogiovanni, Petar Kormushev
A novel skill learning approach is proposed that allows a robot to acquire human-like visuospatial skills for object manipulation tasks. Visuospatial skills are attained by observi…