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Sumeet Singh

ssingh19 _at_ stanford _dot_ edu

Sumeet Singh

Alumni · now at Google DeepMind

Sumeet Singh is a Ph.D. candidate in Aeronautics and Astronautics. He received a B.Eng. in Mechanical Engineering and a Diploma of Music (Performance) from University of Melbourne in 2012, and a M.Sc. in Aeronautics and Astronautics from Stanford University in 2015. Prior to joining Stanford, Sumeet worked in the Berkeley Micromechanical Analysis and Design lab at University of California Berkeley in 2011 and the Aeromechanics Branch at NASA Ames in 2013.

Sumeet’s current research interests are twofold: 1) Robust motion planning for constrained nonlinear systems, and 2) Risk-sensitive Model Predictive Control (MPC). Within the first topic, Sumeet is investigating the design of nonlinear control algorithms for online generation of robust motion plans with guaranteed margins of safety for constrained robotic systems in cluttered environments. The second topic focuses on the development and analysis of stochastic MPC algorithms for robust and risk-sensitive decision making problems.

Awards:

  • Qualcomm Innovation Fellowship (2018)
  • Stanford Graduate Fellowship - Office of Technology Licensing Fellow

Publications

13 publications · full lab bibliography

2023
  1. S. Singh, B. Landry, A. Majumdar, J. J. E. Slotine, and M. Pavone, “Robust Feedback Motion Planning via Contraction Theory,” Int. Journal of Robotics Research, vol. 42, no. 9, pp. 655--688, 2023.
2021
  1. S. Singh, S. M. Richards, V. Sindhwani, J. J. E. Slotine, and M. Pavone, “Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization,” Int. Journal of Robotics Research, vol. 40, no. 10--11, pp. 1123-1150, 2021.
2019
  1. S. Singh, “Robust Control, Planning, and Inference for Safe Robot Autonomy,” Ph.D. dissertation, Stanford University, Dept. of Aeronautics and Astronautics, 2019.
  2. J. Lorenzetti, B. Landry, S. Singh, and M. Pavone, “Reduced Order Model Predictive Control For Setpoint Tracking,” European Control Conference, 2019.
2018
  1. S. Singh, V. Sindhwani, J. J. E. Slotine, and M. Pavone, “Learning Stabilizable Dynamical Systems via Control Contraction Metrics,” Workshop on Algorithmic Foundations of Robotics, 2018.
  2. S. Singh, M. Chen, S. L. Herbert, C. J. Tomlin, and M. Pavone, “Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach,” Workshop on Algorithmic Foundations of Robotics, 2018.
  3. Z. Wang, S. Singh, M. Pavone, and M. Schwager, “Cooperative Object Transport in 3D with Multiple Quadrotors using No Peer Communication,” Proc. IEEE Conf. on Robotics and Automation, 2018.
  4. S. Singh, Y. L. Chow, A. Majumdar, and M. Pavone, “A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms,” IEEE Transactions on Automatic Control, vol. 64, no. 7, pp. 2905--2912, 2018.
  5. S. Singh, J. Lacotte, A. Majumdar, and M. Pavone, “Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods,” Int. Journal of Robotics Research, vol. 37, no. 13, pp. 1713--1740, 2018.
2017
  1. A. Majumdar, S. Singh, A. Mandlekar, and M. Pavone, “Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models,” Robotics: Science and Systems, 2017.
  2. S. Singh, A. Majumdar, J. J. E. Slotine, and M. Pavone, “Robust Online Motion Planning via Contraction Theory and Convex Optimization,” Proc. IEEE Conf. on Robotics and Automation, 2017.
2015
  1. S. Singh, S. D'Amico, and M. Pavone, “High-Fidelity Modeling and Control System Synthesis for a Drag-Free Microsatellite,” Int. Symp. on Space Flight Dynamics, 2015.
  2. S. Singh, E. Schmerling, and M. Pavone, “Decentralized Algorithms for 3D Symmetric Formations in Robotic Networks - A Contraction Theory Approach,” Proc. IEEE Conf. on Robotics and Automation, 2015.