Papers
10
Total Citations
122
H-Index
6
About
Daqing Yi is a leading researcher in human-robot interaction and autonomous motion planning, whose work bridges the gap between how humans naturally communicate and how robots execute complex tasks. His core contributions lie in developing algorithms that enable robots to understand and incorporate human intent, topological constraints, and multiple performance objectives into their planning processes. Yi’s most influential work, "Homotopy-aware RRT*," with 30 citations, pioneered a method for robots to follow human-provided route instructions, such as avoiding certain areas or taking specific paths, by integrating topological reasoning into sampling-based planning. He also advanced multi-objective motion planning with the MORRF framework (17 citations) and introduced a Markov chain Monte Carlo method for sampling Pareto-optimal trajectories (13 citations), allowing robots to balance competing goals like path length, risk, and information gain. His research on task-based mental models using Bayesian approaches (22 citations) and semantic-based path planning for human-robot teams (9 citations) has been instrumental in moving human-robot collaboration from low-level control to high-level, task-oriented teamwork. Yi’s work is essential reading for anyone interested in creating robots that are not just autonomous, but truly collaborative partners.
Research Focus
Key Achievements
Top Papers
- 1Homotopy-aware RRT*: Toward human-robot topological path-planning30 citations · 2016
- 2Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach22 citations · 2013
- 3MORRF: sampling-based multi-objective motion planning17 citations · 2015
- 4Homotopy-Aware RRT*: Toward Human-Robot Topological Path-Planning15 citations · 2016
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- 9Balancing Shared Autonomy with Human-Robot Communication3 citations · 2018
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