Papers
16
Total Citations
581
H-Index
9
About
Ayush Agrawal is a robotics researcher whose work spans safety-critical control, legged locomotion, and assistive robotics. He is best known for extending control barrier functions (CBFs) to the discrete-time domain, a foundational contribution that enables safety guarantees for nonlinear systems — a paper that has amassed over 288 citations and remains a cornerstone reference in the field. His research has significantly advanced bipedal and quadrupedal robot locomotion, particularly in navigating challenging discrete terrain such as stepping stones, using techniques ranging from hybrid zero dynamics (HZD) to stochastic control and model predictive control frameworks. Beyond pure robotics, Agrawal has made meaningful contributions to assistive technology, translating bipedal gait control principles into decentralized exoskeleton control for individuals with lower-limb paralysis — work with direct humanitarian impact, cited over 91 times. His more recent efforts incorporate visual feedback for quadrupedal locomotion, multi-contact manipulation using robot feet, and sequence-agnostic multi-object navigation, reflecting a broadening research vision toward embodied autonomy. Collectively, his publications demonstrate a rigorous blend of mathematical formalism and real-world application, making his work essential reading for students and researchers working at the intersection of control theory and legged robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Dynamic Walking on Randomly-Varying Discrete Terrain with One-step Preview51 citations · 2017
- 4Dynamic bipedal locomotion over stochastic discrete terrain37 citations · 2018
- 5
- 6Online adaptive teleoperation via motion primitives for mobile robots20 citations · 2018
- 7Dynamic Legged Manipulation of a Ball Through Multi-Contact Optimization14 citations · 2020
- 8
- 9Sequence-Agnostic Multi-Object Navigation9 citations · 2023
- 10