31 citations · 109 across the 28 of their papers we have counts for
32 papers
Temporally Interpretable Differentiable Decision Trees
Eisuke Hirota, Aarav Sane, Rohan Paleja
Interpretability offers a solution to safe autonomy by providing transparency into an agent's underlying decision-making model. Within sequential-decision making tasks, differentia…
Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment
Moritz Zoellner, Reece O'Mahoney, Ioannis Havoutis +1
Generalist robot policies such as vision-language-action models (VLAs) have achieved remarkable generalization, but their inference delays can conflict with the demands of real-tim…
Mitigating Retaliatory Algorithmic Collusion in Repeated Games
Karthik Sivachandran, Rohan Paleja
Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling explicit collusion, without commun…
Composing Learned Robot Behaviors with Temporal Logic at Runtime
Moritz Zoellner, Anastasios Manganaris, Ahmed H. Qureshi +1
Executing Linear Temporal Logic (LTL) instructions with learned robot policies faces two practical challenges: demonstrations may cover individual behaviors without containing the…
Temporal Logic Guidance for Action-Only Diffusion Policies with World Models
Moritz Zoellner, Anastasios Manganaris, Rohan Paleja
Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time, even though such control is desirable in human-robot…
Heterogeneous Policy Networks for Composite Robot Team Communication and Coordination
Esmaeil Seraj, Rohan Paleja, Luis Pimentel +7
High-performing human-human teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams implicitly understand the d…
Differentiable Belief-based Opponent Shaping
Aarav G Sane, Karthik Sivachandran, Rohan Paleja
Human coordination often relies on the ability to influence the beliefs of others through strategic action. In multi-agent reinforcement learning, opponent shaping attempts to repl…
Event-Grounded Sparse Autoencoders for Vision-Language-Action Policies
Xinchen Jin, Aditya Chatterjee, Pranav Kumar +1
Vision-Language-Action (VLA) policies translate language and visual inputs into robot actions, where their hidden representations directly shape closed-loop behavior. However, mech…
Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming
Wei Sheng, Rohan Paleja
While AI agents are rapidly advancing from isolated tools to interactive collaborators, data-driven human-machine teaming (HMT) methods remain costly in their reliance on human int…
Enabling Rapid Shared Human-AI Mental Model Alignment via the After-Action Review
Edward Gu, Ho Chit Siu, Melanie Platt +3
In this work, we present two novel contributions toward improving research in human-machine teaming (HMT): 1) a Minecraft testbed to accelerate testing and deployment of collaborat…
Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models
Zhaoxin Li, Zhang Xi-Jia, Batuhan Altundas +3
Semantic interpretability in Reinforcement Learning (RL) enables transparency and verifiability of decision-making. Achieving semantic interpretability in reinforcement learning re…
Asynchronous Training of Mixed-Role Human Actors in a Partially-Observable Environment
Kimberlee Chestnut Chang, Reed Jensen, Rohan Paleja +7
In cooperative training, humans within a team coordinate on complex tasks, building mental models of their teammates and learning to adapt to teammates' actions in real-time. To re…
Faster Model Predictive Control via Self-Supervised Initialization Learning
Zhaoxin Li, Xiaoke Wang, Letian Chen +3
Model Predictive Control (MPC) is widely used in robot control by optimizing a sequence of control outputs over a finite-horizon. Computational approaches for MPC include determini…
STL: Still Tricky Logic (for System Validation, Even When Showing Your Work)
Isabelle Hurley, Rohan Paleja, Ashley Suh +2
As learned control policies become increasingly common in autonomous systems, there is increasing need to ensure that they are interpretable and can be checked by human stakeholder…
Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems
Rohan Paleja, Michael Munje, Kimberlee Chang +2
Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite t…
Why Would You Suggest That? Human Trust in Language Model Responses
Manasi Sharma, Ho Chit Siu, Rohan Paleja +1
The emergence of Large Language Models (LLMs) has revealed a growing need for human-AI collaboration, especially in creative decision-making scenarios where trust and reliance are…
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…
Learning Models of Adversarial Agent Behavior under Partial Observability
Sean Ye, Manisha Natarajan, Zixuan Wu +3
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work,…
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
Zixuan Wu, Sean Ye, Manisha Natarajan +3
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only h…
The Effect of Robot Skill Level and Communication in Rapid, Proximate Human-Robot Collaboration
Kin Man Lee, Arjun Krishna, Zulfiqar Zaidi +5
As high-speed, agile robots become more commonplace, these robots will have the potential to better aid and collaborate with humans. However, due to the increased agility and funct…
Utilizing Human Feedback for Primitive Optimization in Wheelchair Tennis
Arjun Krishna, Zulfiqar Zaidi, Letian Chen +3
Agile robotics presents a difficult challenge with robots moving at high speeds requiring precise and low-latency sensing and control. Creating agile motion that accomplishes the t…
Athletic Mobile Manipulator System for Robotic Wheelchair Tennis
Zulfiqar Zaidi, Daniel Martin, Nathaniel Belles +20
Athletics are a quintessential and universal expression of humanity. From French monks who in the 12th century invented jeu de paume, the precursor to modern lawn tennis, back to t…
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
Rohan Paleja, Muyleng Ghuy, Nadun Ranawaka Arachchige +2
Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to enable humans to gain insight into the decision-making of machine learning models. Despi…
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
Letian Chen, Sravan Jayanthi, Rohan Paleja +3
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, c…