Papers
19
Total Citations
407
H-Index
13
About
Aliakbar Akbari is a leading researcher in autonomous robotics, specializing in task and motion planning for complex manipulation. His work bridges the gap between high-level reasoning and low-level control, enabling robots to perform intricate, physics-aware actions in human environments. Akbari’s major contributions include the development of the Kautham project (71 citations), a widely used teaching and research tool for robot motion planning, and the PMK framework (62 citations), which integrates semantic knowledge for perception and manipulation. He has pioneered combined heuristic approaches for bi-manual robots (35 citations) and physics-based reasoning for task planning (25+ citations), allowing robots to handle geometric constraints, object functionality, and dynamic interactions like pushing or pulling obstacles. His work on ontological physics-based motion planning (29 citations) and knowledge-oriented frameworks for multiple mobile robots (23 citations) has set benchmarks in the field, with evaluation criteria for physics-based planning (18 citations). Akbari’s research, with over 300 total citations, is foundational for advancing autonomous systems in everyday settings, making him a key figure in robotics education and innovation.
Research Focus
Key Achievements
Top Papers
- 1The Kautham project: A teaching and research tool for robot motion planning71 citations · 2014
- 2
- 3Combined heuristic task and motion planning for bi-manual robots35 citations · 2018
- 4Ontological physics-based motion planning for manipulation29 citations · 2015
- 5Task and motion planning using physics-based reasoning25 citations · 2015
- 6Knowledge-oriented task and motion planning for multiple mobile robots23 citations · 2018
- 7
- 8Task planning using physics-based heuristics on manipulation actions19 citations · 2016
- 9Physics-Based Motion Planning: Evaluation Criteria and Benchmarking18 citations · 2015
- 10