Ioannis Dadiotis
Papers
3
Total Citations
30
H-Index
3
About
Ioannis Dadiotis is a rising leader in the field of robotic loco-manipulation, specializing in the control and motion planning of highly redundant legged manipulators. His research focuses on integrating whole-body model predictive control (MPC) and trajectory optimization to enable quadruped robots to perform complex, dynamic tasks while carrying heavy payloads. In his most cited work, he experimentally validated a whole-body MPC framework on a 37 degree-of-freedom dual-arm quadruped, demonstrating unprecedented coordination between locomotion and manipulation—a paper that has already garnered 16 citations since 2023. Dadiotis also developed a payload-aware trajectory optimization formulation that simultaneously plans locomotion and manipulation for heavy-load carrying, earning 8 citations. Most recently, he has advanced the field by applying model-free constrained reinforcement learning to dynamic object pushing with mobile manipulators, addressing real-world uncertainties in object physics and friction. His work bridges the gap between theoretical control and practical deployment, making significant contributions to the next generation of versatile, mobile robots capable of operating in unstructured environments.
Research Focus
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Top Papers
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