Papers
11
Total Citations
114
H-Index
7
About
Vahid Mokhtari is a leading researcher in cognitive robotics, whose work focuses on enabling intelligent service robots to learn, plan, and adapt through accumulated experience. His core research areas include experience-based robot task learning, interactive teaching, and autonomous planning in open-ended environments. Mokhtari’s major contribution is the development of Experience-Based Planning Domains (EBPDs), a framework that allows robots to gather, conceptualize, and reuse activity experiences to improve task performance without explicit reprogramming. His work on the RACE Project (25 citations) and interactive teaching methods (22 citations) demonstrates how human-robot interaction can guide experience acquisition and concept learning. Notably, his research on learning robot tasks with loops (9 citations) and autonomous runtime composition of sensor-based skills (8 citations) addresses key challenges in robot adaptability and robustness. Mokhtari’s Safe-Planner (4 citations) further advances nondeterministic planning by computing strong cyclic policies. With over 100 total citations across his publications, his integrated approach to learning and deliberation continues to influence the development of more autonomous, capable, and human-friendly robotic systems.
Research Focus
Key Achievements
Top Papers
- 1The RACE Project25 citations · 2014
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- 3Experience-Based Robot Task Learning and Planning with Goal Inference12 citations · 2016
- 4Gathering and Conceptualizing Plan-Based Robot Activity Experiences12 citations · 2015
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- 9An approach to robot task learning and planning with loops4 citations · 2017
- 10