Oliver Roesler
Vrije Universiteit Brussel, University of Reading, Schüßler-Plan (Germany)
Papers
7
Total Citations
86
H-Index
4
About
Oliver Roesler is a leading researcher at the intersection of social robotics, natural language processing, and human-robot interaction (HRI). His work focuses on enabling robots to understand and interact with humans more naturally, with a particular emphasis on cognitive empathy and language grounding. Roesler’s most impactful contribution is his pioneering development of a reinforcement learning-based cognitive empathy framework for social robots (2020, 44 citations), which allows robots to perceive and respond to human emotional states. He has also made significant strides in grounding natural language instructions, introducing probabilistic models that enable robots to link words to objects and actions—including handling unknown synonyms, a challenge largely overlooked in prior work (2019, 18 citations; 2018, 9 citations). His research extends to action learning in simulated HRI (2019, 7 citations) and evaluating SLAM algorithms for dynamic environments (2019, 4 citations). More recently, Roesler has explored the effects of socially aware robot behavior (2022, 2 citations) and continues to advance frameworks for learning cognitive empathy (2020, 2 citations). With over 86 total citations, Roesler’s work is shaping the future of empathetic, linguistically capable social robots.
Research Focus
Key Achievements
Top Papers
- 1A Reinforcement Learning Based Cognitive Empathy Framework for Social Robots44 citations · 2020
- 2
- 3A Probabilistic Framework for Comparing Syntactic and Semantic Grounding of Synonyms through Cross-Situational Learning9 citations · 2018
- 4Action learning and grounding in simulated human–robot interactions7 citations · 2019
- 5Evaluation of SLAM Algorithms for Highly Dynamic Environments4 citations · 2019
- 6Toward understanding the effects of socially aware robot behavior2 citations · 2022
- 7