Ali Hammoud
Papers
6
Total Citations
35
H-Index
3
About
Ali Hammoud’s research bridges the gap between human dexterity and robotic manipulation, with a focus on hand-object interaction, swarm robotics, and tactile sensing. His most influential work, “Hand-Object Interaction: From Human Demonstrations to Robot Manipulation” (24 citations), lays the foundation for translating human hand skills into robotic capabilities—a critical step for robots operating in human environments. Hammoud further advanced this area by developing a robotic in-hand manipulation dictionary from human data (3 citations) and a motion planning framework using human motion primitives (2 citations), enabling more natural and adaptive robotic grasping. In swarm robotics, he introduced a hybrid modular architecture combining deep reinforcement learning and particle swarm optimization for dynamic foraging (4 citations), and explored inverse reinforcement learning to decode collective swarm behaviors (1 citation). His recent work on online object localization via tactile sensing (1 citation) addresses a key limitation of vision-based approaches in cluttered settings. With a total of 35 citations across six publications from 2021 to 2025, Hammoud’s contributions are shaping the next generation of dexterous, swarm-capable robots that learn from and interact with humans.
Research Focus
Key Achievements
Top Papers
- 1Hand-Object Interaction: From Human Demonstrations to Robot Manipulation24 citations · 2021
- 2
- 3A robotic in-hand manipulation dictionary based on human data3 citations · 2021
- 4In-hand manipulation planning using human motion dictionary2 citations · 2022
- 5
- 6Online object localization in a robotic hand by tactile sensing1 citations · 2025