10 citations · 12 across the 8 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…