10 citations · 12 across the 11 of their papers we have counts for
11 papers
Beyond Fixed Goal Delivery: Online POMDP Planning for Target Interception in Crowds
Himanshu Gupta, Kelvin Aladum, Nisar Ahmed +2
Target interception in crowded environments requires reaching a moving objective while navigating among multiple uncertain human agents. Since human navigation intent is not direct…
Environment Design for Reliable Shared Autonomy with Probabilistic Guarantees
Yi-Shiuan Tung, Himanshu Gupta, Gyanig Kumar +3
Shared autonomy enables humans and robots to collaboratively perform tasks by combining human input with autonomous assistance. Most prior work focuses on improving intent inferenc…
Risk-Aware Preference Learning for Stochastic Outcomes
Yi-Shiuan Tung, Yuni Wu, Wei Jiang +2
Learning reward functions from human preferences is a widely used approach for aligning robot behavior with user expectations in human-robot interaction. Most existing approaches a…
Efficient Multi-Robot Motion Planning with Precomputed Translation-Invariant Edge Bundles
Himanshu Gupta, Paul Motter, Aritra Chakrabarty +5
Solving multi-robot motion planning (MRMP) requires generating collision-free kinodynamically feasible trajectories for multiple interacting robots. We introduce Kinodynamic Transl…
CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning
Yi-Shiuan Tung, Gyanig Kumar, Wei Jiang +2
As a robot's operational environment and tasks to perform within it grow in complexity, the explicit specification and balancing of optimization objectives to achieve a preferred b…
ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors
Shivendra Agrawal, Jake Brawer, Ashutosh Naik +2
Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and percep…
Counterfactual Reasoning and Environment Design for Active Preference Learning
Yi-Shiuan Tung, Bradley Hayes, Alessandro Roncone
For effective real-world deployment, robots should adapt to human preferences, such as balancing distance, time, and safety in delivery routing. Active preference learning (APL) le…
Employing Laban Shape for Generating Emotionally and Functionally Expressive Trajectories in Robotic Manipulators
Srikrishna Bangalore Raghu, Clare Lohrmann, Akshay Bakshi +4
Successful human-robot collaboration depends on cohesive communication and a precise understanding of the robot's abilities, goals, and constraints. While robotic manipulators offe…
Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration
Yi-Shiuan Tung, Matthew B. Luebbers, Alessandro Roncone +1
Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to acco…
Improving Human Legibility in Collaborative Robot Tasks through Augmented Reality and Workspace Preparation
Yi-Shiuan Tung, Matthew B. Luebbers, Alessandro Roncone +1
Understanding the intentions of human teammates is critical for safe and effective human-robot interaction. The canonical approach for human-aware robot motion planning is to first…
Towards A Natural Language Interface for Flexible Multi-Agent Task Assignment
Jake Brawer, Kayleigh Bishop, Bradley Hayes +1
Task assignment and scheduling algorithms are powerful tools for autonomously coordinating large teams of robotic or AI agents. However, the decisions these system make often rely…