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Ya-Chuan Hsu

4 accepted papers

2026

Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization

ICML 2026poster

AI assistants in human-AI collaboration often correct suboptimal human actions through behavioral feedback (e.g., alerts or steering-wheel nudges in assistive driving). Such interventions can mitigate immediate errors, but long-term improvement requires addressing the underlying misconceptions that …

Cited by 0SourceScholar
2025

Integrating Field of View in Human-Aware Collaborative Planning

ICRA 2025

In human-robot collaboration (HRC), it is crucial for robot agents to consider humans' knowledge of their surroundings. In reality, humans possess a narrow field of view (FOV), limiting their perception. However, research on HRC often overlooks this aspect and presumes an omniscient human collaborat

Cited by 3SourceScholar
2023

Surrogate Assisted Generation of Human-Robot Interaction Scenarios

CoRL 2023oral

As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments and with different users. To this end, previous methods have algorithmically generated diverse scenarios that reveal syste…

Cited by 11SourcecodeScholar
2020

A POMDP Treatment of Vehicle-Pedestrian Interaction: Implicit Coordination via Uncertainty-Aware Planning

IROS 2020poster

Drivers and other road users often encounter situations (e.g., arriving at an intersection simultaneously) where priority is ambiguous or unclear but must be resolved via communication to reach agreement. This poses a challenge for autonomous vehicles, for which no direct means for expressing intent…

Cited by 11SourceScholar