Welcome to the Autonomous Systems and Biomechatronics Laboratory. We are part of the Mechanical and Industrial Engineering Department of the University of Toronto. The lab is directed by Professor Goldie Nejat, who is also the Canada Research Chair in Robots for Society.

Our research focuses on developing intelligent mechatronics and robotic systems to assist humans in dangerous and stressful tasks and/or when a shortage of qualified personnel exists. In particular, our research is dedicated to the development of intelligent mechatronics systems with a primary focus on the design of robots and devices. Examples include the design of intelligent robotic systems, sensor agents and devices for search and rescue, exploration, surveillance, human-robot interaction, medical and health care applications.

Our research team is highly interdisciplinary consisting of researchers from both the applied and health sciences.

Research Highlight Papers

Mobile Robot Navigation and Exploration

A. H. Tan, S. Narasimhan, G. Nejat, "4CNet: A Confidence-Aware, Contrastive, Conditional, Consistency Model for Robot Map Prediction in Multi-Robot Environments," arXiv, 2024 (Paper)

H. Wang, A. H. Tan, and G. Nejat, “NavFormer: A Transformer Architecture for Robot Target-Driven Navigation in Unknown and Dynamic Environments,”  arXiv, 2024. (Paper)

A. H. Tan, F. P. Bejarano, Y. Zhu, R. Ren, and G. Nejat, “Deep Reinforcement Learning for Decentralized Multi-Robot Exploration with Macro Actions,”  In IEEE Robotics and Automation Letters, vol. 8, no. 1, pp. 272-279, Jan. 2023. (pdf)

A. H. Tan, and G. Nejat, "Enhancing Robot Task Completion Through Environment and Task Inference: A Survey from the Mobile Robot Perspective,” Journal of Intelligent and Robotic Systems, Vol. 106, Iss. 73, 2022. (pdf)

H. Hu, K. Zhang, A. H. Tan, M. Ruan, C. Agia, and G. Nejat, “A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain,” IEEE Robot. Autom. Lett., vol. 6, no. 4, pp. 6569–6576, 2021. (pdf)

F. Niroui, K. Zhang, Z. Kashino, and G. Nejat, “Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments,” IEEE Robot. Autom. Lett., vol. 4, no. 2, pp. 610–617, 2019.(pdf)

K. Zhang, F. Niroui, M. Ficocelli and G. Nejat, “Robot Navigation of Environments with Unknown Rough Terrain Using Deep Reinforcement Learning,” IEEE International Symposium on Safety, Security, and Rescue Robotics, 2018. (Best Student Paper Award) (pdf)

Robot Perception

A. Fung, B. Benhabib and G. Nejat, “LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models,” arXiv, 2024. (Paper)

A. Fung, B. Benhabib and G. Nejat, “Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations,” IEEE Robotics and Automation Letters (RA-L), vol. 8, no. 6, pp. 3550-3557, June 2023. (pdf)

D. Dworakowski, A. Fung, and G. Nejat, "Robots Understanding Contextual Information in Human-Centered Environments Using Weakly Supervised Mask Data Distillation,” International Journal of Computer Vision, 2022. (pdf)

Socially Assistive Robots

F. Robinson, Z. Cen, H. Naguib, and G. Nejat, “An intelligent socially assistive robot-wearable sensors system for personalized user dressing assistance,” Advanced Robotics, June 2023.

S. C. Mohamed, A. Fung and G. Nejat, "A Multirobot Person Search System for Finding Multiple Dynamic Users in Human-Centered Environments," in IEEE Transactions on Cybernetics, vol. 53, no. 1, pp. 628-640, Jan. 2023. (pdf)

F. Robinson and G. Nejat, "A Deep Learning Human Activity Recognition Framework for Socially Assistive Robots to Support Reablement of Older Adults," 2023 IEEE International Conference on Robotics and Automation (ICRA). (pdf)

M. Shao, M. Pham-Hung, S.F.R. Alves, M. Snyder, K. Eshaghi, B. Benhabib, G. Nejat, “Long-Term Exercise Assistance in Group & One-on-One Interactions with a Social Robot & Older Adults,” Robotics, vol. 12, iss. 1, 2023. (pdf)

C. Getson and G. Nejat, “The Robot Screener Will See You Now: A Socially Assistive Robot for COVID-19 Screening in Long-Term Care Homes,” 2022 IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). (pdf)

C. Getson and G. Nejat  "The Adoption of Socially Assistive Robots for Long-term Care: During the Pandemic and in a Post-COVID-19 Society," Healthcare Management Forum Special edition on Aging, Technology and Health in a Post-COVID World, Healthcare Management Forum, Vol. 0(0) 1–9, 2022. (pdf)

D. Dworakowski, C. Thompson, M. Pham-Hung and G. Nejat “A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments,” Robotics, Special Issue on 10th Anniversary of Robotics (pdf)

C. Getson, and G. Nejat, "Socially Assistive Robots Helping Older Adults through the Pandemic and Life after COVID-19," Robotics 2021, 10, 106. (pdf)

Social Human-Robot Interaction

S. Saunderson and G. Nejat, "Investigating Strategies for Robot Persuasion in Social Human-Robot Interaction," IEEE Transactions on Cybernetics, vol. 52, no. 1, pp. 641-653, Jan. 2022. (pdf)

S. Saunderson and G. Nejat, "Persuasive robots should avoid authority: The effects of formal and real authority on persuasion in human-robot interaction," Science Robotics, vol. 6, no. 58, eabd5186 (2021). (pdf)

S. Saunderson and G. Nejat, “Robots Asking for Favors: The Effects of Directness and Familiarity on Persuasive HRI,” IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 2, pp. 1793-1800, April 2021. (pdf)

S. Saunderson and G. Nejat, "Investigating Strategies for Robot Persuasion in Social Human-Robot Interaction," in IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2020.2987463. pp. 1-13, May 2020. (pdf)

Swarm Robotics

A. Rogers, K. Eshaghi, G. Nejat, B. Benhabib, “Occupancy grid mapping via resource-constrained robotic swarms: A collaborative exploration strategy,” Robotics, vol. 2, iss. 3, 2023. (pdf)

K. Eshaghi, A. Rogers, G. Nejat, and B. Benhabib, “Closed-Loop Motion Control of Robotic Swarms – A Tether-Based Strategy,” in IEEE Transactions on Robotics, 2022, doi: 10.1109/TRO.2022.3181055. (pdf)

K. Eshaghi, Y. Li, Z. Kashino, G. Nejat, B. Benhabib, “mROBerTO 2.0 - An Autonomous milli-Robot with Enhanced Locomotion for Swarm Robotics,” IEEE Robotics and Automation Letters (RA-L), vol. 5, no. 2, pp. 962-969, April 2020. (pdf)

Research Highlight Videos

Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations

Deep Reinforcement Learning for Decentralized Multi-Robot Exploration with Macro Actions

A Deep Learning Activity Recognition for Social Robots to Support Reablement of Older Adults

A Robot Architecture Using ContextSLAM to Find Products in Unknown Crowded Retail Environments

A Sim-to-Real Pipeline for Deep Reinforcement Learning for Autonomous Robot Navigation in Cluttered Rough Terrain

Persuasive robots should avoid authority: The effects of formal and real authority on persuasion in HRI

Robots Asking for Favors: The Effect of Directness and Familiarity on Persuasive HRI

Robot Navigation of Environments with Unknown Rough Terrain Using Deep Reinforcement Learning

Blueberry Robot: Real-World Robot Search Experiment

mROBerTO: A Modular Millirobot for Swarm-Behavior Studies

Tangy the Socially Assistive Robot Facilitating a Bingo Game:

Casper the Friendly Robot Assisting in the Home:

Leia, the Social Robot Providing Clothing Assistance: