Papers
11
Total Citations
151
H-Index
6
About
Bahareh Abbasi is a robotics researcher whose work sits at the intersection of human-robot interaction, physical manipulation, and assistive robotics. She has made significant contributions to the science of robot-human object handover, developing models that replicate natural human reaching behavior and designing fail-safe controllers that minimize transfer failures — work that has collectively garnered over 85 citations. Her 2017 paper modeling the human reaching phase in handover tasks (44 citations) stands as a landmark contribution, offering principled frameworks for selecting safe, comfortable transfer configurations and trajectories. Abbasi has also advanced the understanding of human grasping through force-distribution-based taxonomies, providing practical tools for programming assistive robots through demonstration. More recently, her research has expanded into multimodal human-robot collaboration, where she has developed interaction managers capable of handling multiple communication channels simultaneously and reinforcement learning agents that enable robots to dynamically switch roles during collaborative tasks. Her 2023 work on neural network-based human simulators reflects a forward-looking approach to training adaptive robot assistants. Across her career, Abbasi's research has been consistently motivated by a vision of robots that can meaningfully support older adults and people with disabilities in daily living activities.
Research Focus
Key Achievements
Top Papers
- 1
- 2Failure Recovery in Robot–Human Object Handover23 citations · 2018
- 3Grasp taxonomy based on force distribution22 citations · 2016
- 4A fail-safe object handover controller19 citations · 2016
- 5A Multimodal Human-Robot Interaction Manager for Assistive Robots16 citations · 2019
- 6Role Switching in Task-Oriented Multimodal Human-Robot Collaboration12 citations · 2020
- 7
- 8
- 9
- 10Multimodal Reinforcement Learning for Robots Collaborating with Humans2 citations · 2023