Shubham Singh

The University of Texas at Austin

Papers

1

Total Citations

8

H-Index

1

About

Shubham Singh is a robotics researcher specializing in optimal control, contact dynamics, and motion generation for legged robots. His work sits at the intersection of trajectory optimization and rigid-body dynamics, with a particular focus on advancing the mathematical foundations that enable more efficient and robust robot locomotion. Singh's most notable contribution to date is his 2023 paper on analytical second-order derivatives of rigid-body contact dynamics, applied to multi-shooting Differential Dynamic Programming (DDP). This work addresses a meaningful gap in the field: while DDP is a widely adopted technique for generating dynamic legged robot motion, practitioners have largely relied on first-order partial derivatives — the iLQR approximation — neglecting second-order terms that can significantly improve convergence and solution quality. By deriving and incorporating these analytical second-order derivatives, Singh's research pushes toward more accurate and theoretically complete optimization frameworks for contact-rich robotic systems. The paper has garnered 8 citations since its publication, reflecting growing interest from the trajectory optimization and legged robotics communities. His research is particularly relevant for students and engineers working on humanoids, quadrupeds, and other dynamic systems where precise contact modeling is critical to achieving reliable, agile motion.

Research Focus

Key Achievements

1
H-Index
1
Papers
8
Total Citations
8
Avg Citations/Paper
🏆 Most Cited Paper
Analytical Second-Order Derivatives of Rigid-Body Contact Dynamics: Application to Multi-Shooting DDP
8 citations · 2023
📈 Most Prolific Year: 2023 (1 Papers)
🤝 Key Collaborators: 2
🏛 Institutions: The University of Texas at Austin

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago