A method automatically constructs a causal model from behavior tree structure and domain knowledge to generate real-time causal counterfactual explanations for robot decisions.
Evaluating efficiency and engagement in scripted and LLM-enhanced human-robot interactions
7 Pith papers cite this work. Polarity classification is still indexing.
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A breathing companion robot reduced perceived stress more than audio guidance alone in 14 Ukrainian refugees, with mixed physiological outcomes and three identified breathing patterns.
QuickLAP fuses LLM-extracted language observations with physical feedback in a closed-form Bayesian update to cut reward learning error by over 70% in a driving simulator and improve user preference in a 15-person study.
Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface.
Medium personality expression in LLM agents yields the most positive user perceptions in goal-oriented tasks, further improved by trait alignment.
A scoping review and personal reflections identify robot wrangling as a complex umbrella term and generate design implications for supporting wranglers as individuals and within broader service ecologies.
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.
citing papers explorer
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Temporal Counterfactual Explanations of Behaviour Tree Decisions
A method automatically constructs a causal model from behavior tree structure and domain knowledge to generate real-time causal counterfactual explanations for robot decisions.
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NEFFY 2.0: A Breathing Companion Robot: User-Centered Design and Findings from a Study with Ukrainian Refugees
A breathing companion robot reduced perceived stress more than audio guidance alone in 14 Ukrainian refugees, with mixed physiological outcomes and three identified breathing patterns.
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QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents
QuickLAP fuses LLM-extracted language observations with physical feedback in a closed-form Bayesian update to cut reward learning error by over 70% in a driving simulator and improve user preference in a 15-person study.
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Distill: Uncovering the True Intent behind Human-Robot Communication
Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface.
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Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks
Medium personality expression in LLM agents yields the most positive user perceptions in goal-oriented tasks, further improved by trait alignment.
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Designing for Robot Wranglers: A Synthesis of Literature and Practice
A scoping review and personal reflections identify robot wrangling as a complex umbrella term and generate design implications for supporting wranglers as individuals and within broader service ecologies.
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Software Engineering for Self-Adaptive Robotics: A Research Agenda
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.