Mitchell Abrams
Papers
5
Total Citations
29
H-Index
2
About
Mitchell Abrams is a leading researcher in human-robot interaction, specializing in transparent communication, social norm integration, and situated dialogue systems. His work addresses a critical challenge: enabling robots to understand not just what humans say, but whether an instruction is appropriate, authorized, and contextually grounded. In his highly cited 2022 paper, "Transparency through Explanations and Justifications in Human-Robot Task-Based Communications" (18 citations), Abrams pioneered frameworks for robots to assess and explain their compliance with human commands, enhancing trust and safety. His 2022 study on "Social Norms Guide Reference Resolution" (6 citations) demonstrated how pragmatic and social cues resolve ambiguous language in real-world settings, a breakthrough for collaborative robotics. Abrams also developed the SCOUT corpus (2024), a multi-modal human-robot dialogue dataset that serves as a foundational resource for the field. His innovative use of Abstract Meaning Representation for graph-to-graph transformations (2020) bridges natural language and robot action specifications. With recent work on norm-based reference resolution (2025), Abrams continues to shape how robots navigate complex social and task environments, making him a pivotal figure in advancing autonomous, socially-aware systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Social Norms Guide Reference Resolution6 citations · 2022
- 3
- 4SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus2 citations · 2024
- 5Robots That Perform Norm-Based Reference Resolution1 citations · 2025