Bo Liu

About

Bo Liu is a robotics and artificial intelligence researcher whose work sits at the intersection of autonomous navigation, machine learning, and human-robot interaction. He is best known for his pioneering contributions to adaptive robot navigation, particularly through the development of the Adaptive Planner Parameter Learning (APPL) family of frameworks — including APPLD, APPLI, and APPL — which enable robots to intelligently tune their navigation parameters through demonstration and human intervention, rather than requiring repeated manual reconfiguration by expert engineers. His 2022 survey on machine learning for mobile robot navigation has rapidly become a foundational reference in the field, amassing over 234 citations. Liu's lifelong learning framework for mobile robots addresses a critical challenge in real-world deployment: enabling systems to continuously self-improve across diverse environments rather than repeating past errors. His innovative "Learning from Hallucination" paradigm allows robots to master agile maneuvering in tightly constrained spaces by training on artificially enriched obstacle scenarios. Beyond navigation, Liu has contributed meaningfully to human gaze-assisted AI and human-robot collaboration decision-making. With nearly 650 cumulative citations across his published work, his research is reshaping how autonomous robots learn, adapt, and operate safely alongside humans.

Research Focus

Key Achievements

Top Papers

1. 1
   Motion planning and control for mobile robot navigation using machine learning: a survey

   234 citations · 2022
2. 2
   A Lifelong Learning Approach to Mobile Robot Navigation

   97 citations · 2021
3. 3
   APPLD: Adaptive Planner Parameter Learning From Demonstration

   66 citations · 2020
4. 4
   Toward Agile Maneuvers in Highly Constrained Spaces: Learning From Hallucination

   51 citations · 2021
5. 5
   Human Gaze Assisted Artificial Intelligence: A Review

   47 citations · 2020
6. 6
   APPL: Adaptive Planner Parameter Learning

   43 citations · 2022
7. 7
   APPLI: Adaptive Planner Parameter Learning From Interventions

   40 citations · 2021
8. 8
   Motion Control for Mobile Robot Navigation Using Machine Learning: a Survey

   31 citations · 2020
9. 9
   Agile Robot Navigation through Hallucinated Learning and Sober Deployment

   28 citations · 2021
10. 10
    Research on Cobot Action Decision-Making Method Based on Intuitionistic Fuzzy Set and Game Theory

    9 citations · 2022

Key Collaborators

Contact & Links