Papers
13
Total Citations
168
H-Index
6
About
Jung-Su Ha is a leading researcher in robot manipulation planning, physical reasoning, and stochastic optimal control. His work bridges the gap between high-level task planning and low-level motion control, with a particular focus on integrating deep learning into manipulation planning frameworks. Ha’s most impactful contribution is his 2020 paper "Deep Visual Heuristics," which introduced a deep neural network that predicts the feasibility of mixed-integer programs from visual input—a breakthrough that has garnered 52 citations and opened new avenues for learning-based task and motion planning. He is also known for his force-based sequential manipulation planning approach, which uses physical reasoning models to enable robots to reason about contact dynamics and tool use, as detailed in his highly cited 2020 paper (38 citations). Ha’s work extends to co-optimizing robot, environment, and tool design (21 citations), and he has made notable contributions to informative path planning with multiple UAVs in wind fields. His research, published in top venues like IEEE and RSS, has earned over 150 total citations, establishing him as a key figure in advancing intelligent robotic systems that can reason, plan, and adapt in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Informative Path Planning and Mapping with Multiple UAVs in Wind Fields14 citations · 2018
- 5A topology-guided path integral approach for stochastic optimal control11 citations · 2016
- 6
- 7
- 8
- 9
- 10Informative Path Planning and Mapping with Multiple UAVs in Wind Fields4 citations · 2016