Papers
12
Total Citations
84
H-Index
6
About
Joaquim Ortiz-Haro is a robotics researcher whose work sits at the intersection of task and motion planning, kinodynamic motion planning, and multi-robot coordination. His research tackles some of the field's most computationally demanding challenges: enabling robots to reason simultaneously about what to do and how to do it, while respecting complex dynamic constraints and real-world physical limitations. Among his most recognized contributions are the RHH-LGP framework, which addresses combinatorial complexity in long-horizon manipulation tasks through receding-horizon heuristics within Logic-Geometric Programming, and a suite of kinodynamic planners — including db-A*, db-CBS, iDb-RRT, and iDb-A* — that dramatically improve motion planning efficiency for dynamically constrained systems such as multirotors and differential-drive robots. His multi-robot work extends these ideas to teams of UAVs performing cable-suspended payload transport in cluttered environments, a problem of significant practical relevance. With papers accumulating citations rapidly since 2022, and contributions spanning conflict-driven symbolic-continuous interfaces, diverse planning, and Model Predictive Control with learned value functions, Ortiz-Haro has established himself as a productive and technically rigorous voice in modern robot planning research, bridging the gap between theoretical optimality and real-world deployability.
Research Focus
Key Achievements
Top Papers
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- 6Conflict-Directed Diverse Planning for Logic-Geometric Programming6 citations · 2022
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