Daniel Gonzalez-Diaz
Papers
2
Total Citations
36
H-Index
2
About
Daniel Gonzalez-Diaz is a robotics researcher whose work sits at the intersection of whole-body control, safety-critical systems, and real-time optimization for humanoid robots. His most impactful contribution is the integration of Control Barrier Functions (CBFs) with whole-body controllers to guarantee self-collision avoidance—a fundamental safety challenge for humanoid platforms. His 2022 paper on this topic, which has already garnered 33 citations, demonstrates how leveraging the robot's full dynamics can provably prevent self-collisions, moving beyond heuristic reactive methods. This work, validated on the MIT Humanoid, represents a significant step toward deploying humanoid robots in human-centric environments. More recently, Gonzalez-Diaz has advanced the computational efficiency of Model Predictive Control (MPC) through his work on Model Hierarchy Predictive Control, which optimally schedules different dynamics models across the planning horizon to balance fidelity and real-time performance. His research is notable for its rigorous theoretical grounding combined with practical hardware validation, making him a rising figure in the field of legged locomotion and safety-aware control.
Research Focus
Key Achievements
Top Papers
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