Peter Ohler
Papers
3
Total Citations
19
H-Index
2
About
Peter Ohler’s research lies at the intersection of human-robot interaction, virtual agents, and affective computing, where he investigates how design and behavior shape trust, credibility, and collaboration. His most cited work, “Uncanny…But Convincing? Inconsistency Between a Virtual Agent’s Facial Proportions and Vocal Realism Reduces Its Credibility and Attractiveness, but Not Its Persuasive Success” (2018, 14 citations), reveals a striking paradox: mismatched visual and vocal realism can harm an agent’s likability without undermining its persuasive power—a key insight for designing effective digital assistants. In earlier work, Ohler developed an emotional model enabling robots to deduce human emotions from non-verbal actions during joint tasks (2014, 3 citations), advancing socially aware automation for industrial settings. His most recent contribution (2025, 2 citations) tackles motion legibility in robot arms, proposing human-like sampling strategies that overcome limitations of optimization-based approaches, thereby improving human-robot collaboration. Though early in his career, Ohler’s focus on the subtle interplay between realism, emotion, and motion legibility marks him as a thoughtful contributor to creating machines that are not only functional but also intuitively understandable and trustworthy.
Research Focus
Key Achievements
Top Papers
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