Papers
11
Total Citations
365
H-Index
9
About
Hao Dang’s research lies at the intersection of robotic grasping, tactile perception, and task-driven manipulation. His core contributions center on enabling robots to plan and execute grasps that are not only stable but also functionally suitable for specific object manipulation tasks—a concept he terms “semantic grasping.” Dang pioneered the use of tactile feedback and hand kinematic data as proxies for semantic constraints, allowing robots to grasp objects without relying on visual or geometric information, a method known as “blind grasping.” His work on learning grasp stability from tactile data has been highly influential, with his most-cited paper, “Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task” (2012), accumulating 89 citations. He has also advanced robot learning from human demonstration, developing task descriptors and program synthesis techniques for everyday object manipulations. Dang’s research has practical implications for disaster response, where he has explored complex physical task planning for humanoid robots. With over 360 total citations across his top publications, Hao Dang’s work has significantly shaped the fields of robotic manipulation and tactile-based grasping, providing foundational methods for creating more dexterous and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Learning grasp stability61 citations · 2012
- 4Semantic grasping: planning task-specific stable robotic grasps37 citations · 2014
- 5Robot learning of everyday object manipulations via human demonstration32 citations · 2010
- 6
- 7Planning complex physical tasks for disaster response with a humanoid robot15 citations · 2013
- 8Program synthesis by examples for object repositioning tasks11 citations · 2014
- 9Functional analysis of finger contact locations during grasping11 citations · 2009
- 10Kinematic Analysis and Simulation of a Cockroach Robot4 citations · 2007