Assistive Robot
Assistive robots aim to enhance the independence and quality of life for individuals with disabilities or age-related limitations by providing physical and cognitive support in daily tasks. Current research emphasizes improving robot control through multimodal integration of biosignals, vision, and language, often employing deep reinforcement learning, vision-language models, and large language models to achieve robust and personalized assistance. This field is significant for its potential to address critical societal needs, particularly in aging populations and healthcare, by developing safe, reliable, and user-friendly robotic systems that promote autonomy and reduce caregiver burden.
Papers
Towards Robust Perception for Assistive Robotics: An RGB-Event-LiDAR Dataset and Multi-Modal Detection Pipeline
Adam Scicluna, Cedric Le Gentil, Sheila Sutjipto, Gavin Paul
Do Mistakes Matter? Comparing Trust Responses of Different Age Groups to Errors Made by Physically Assistive Robots
Sasha Wald, Kavya Puthuveetil, Zackory Erickson
Improving Robotic Arms through Natural Language Processing, Computer Vision, and Edge Computing
Pascal Sikorski, Kaleb Yu, Lucy Billadeau, Flavio Esposito, Hadi AliAkbarpour, Madi Babaiasl
Socially-Aware Shared Control Navigation for Assistive Mobile Robots in the Built Environment
Yifan Xu, Qianwei Wang, Vineet Kamat, Carol Menassa