Papers
9
Total Citations
476
H-Index
8
About
Yanran Ding is a leading researcher in dynamic legged locomotion and humanoid robotics, whose work has profoundly shaped how robots run, jump, and maintain balance. Her primary research areas span model predictive control (MPC), real-time optimization, and whole-body control for quadrupedal and humanoid platforms. Ding’s most influential contribution is the development of representation-free MPC (RF-MPC), which directly uses rotation matrices to control dynamic motions—a breakthrough cited over 170 times that frees robots from the singularities and complexities of traditional attitude representations. Her 2019 MPC framework for versatile quadrupedal motions (102 citations) and the qpSWIFT solver (85 citations) have become essential tools for real-time robotic control. Notably, she co-designed the MIT Humanoid, a high-power research platform built for athletic feats like running and jumping. Ding also pioneered the use of control barrier functions for self-collision avoidance in humanoids and created HOPPY, an open-source educational hopping robot kit. With over 450 total citations across her key works, Yanran Ding’s research continues to push the boundaries of what dynamic robots can achieve.
Research Focus
Key Achievements
Top Papers
- 1Representation-Free Model Predictive Control for Dynamic Motions in Quadrupeds173 citations · 2021
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- 5Single Leg Dynamic Motion Planning with Mixed-Integer Convex Optimization24 citations · 2018
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- 7Centroidal-momentum-based trajectory generation for legged locomotion16 citations · 2020
- 8HOPPY: An Open-source Kit for Education with Dynamic Legged Robots14 citations · 2021
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