2 citations · 3 across the 5 of their papers we have counts for
5 papers
Learning to Plan in Human-Robot Collaboration: Multimodal Reinforcement Learning for Adaptive Interaction
Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul +3
Robot assistants for older adults and people with disabilities need to perform collaborative tasks with users effectively. The core component of these systems is an interaction man…
An End-to-End Human Simulator for Task-Oriented Multimodal Human-Robot Collaboration
Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul +3
This paper proposes a neural network-based user simulator that can provide a multimodal interactive environment for training Reinforcement Learning (RL) agents in collaborative tas…
Multimodal Reinforcement Learning for Robots Collaborating with Humans
Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul +3
Robot assistants for older adults and people with disabilities need to interact with their users in collaborative tasks. The core component of these systems is an interaction manag…
Evaluating Multimodal Interaction of Robots Assisting Older Adults
Afagh Mehri Shervedani, Ki-Hwan Oh, Bahareh Abbasi +4
We outline our work on evaluating robots that assist older adults by engaging with them through multiple modalities that include physical interaction. Our thesis is that to increas…
Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data
Bahareh Abbasi, Ehsan Noohi, Sina Parastegari +1
Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data…