Somrita Banerjee

Somrita Banerjee

Somrita Banerjee is a Ph.D. candidate in Aeronautics and Astronautics. She received her B.S. in Mechanical Engineering with minors in Aerospace Engineering and Computer Science from Cornell University in 2017. At Cornell University, she worked in the Space Systems Design Studio with Professor Mason Peck.

Somrita’s current research interests lie at the intersection of trajectory optimization, machine learning, and optimal control of the next generation of space robots, specifically to further goals of greater autonomy and risk-sensitive learning.

In her free time, Somrita enjoys dancing, playing board games with friends, and going hiking in sunny California.


  • Stanford Graduate Fellowship

ASL Publications

  1. S. Banerjee, J. Harrison, P. M. Furlong, and M. Pavone, “Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics,” in Int. Symp. on Artificial Intelligence, Robotics and Automation in Space, Pasadena, California, 2020.

    Abstract: Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. Terrain type classification relies on input from human operators or machine learning-based image classification algorithms. However, high level terrain classification is typically not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap; in these situations, online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover. This paper presents a meta-learning-based approach to adapt probabilistic predictions of rover dynamics by augmenting a nominal model affine in parameters with a Bayesian regression algorithm (P-ALPaCA). A regularization scheme is introduced to encourage orthogonality of nominal and learned features, leading to interpretable probabilistic estimates of terrain parameters in varying terrain conditions.

      author = {Banerjee, S. and Harrison, J. and Furlong, P. M. and Pavone, M.},
      title = {Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics},
      booktitle = {{Int. Symp. on Artificial Intelligence, Robotics and Automation in Space}},
      year = {2020},
      address = {Pasadena, California},
      month = oct,
      url = {},
      owner = {somrita},
      timestamp = {2020-09-18}
  2. S. Banerjee, T. Lew, R. Bonalli, A. Alfaadhel, I. A. Alomar, H. M. Shageer, and M. Pavone, “Learning-based Warm-Starting for Fast Sequential Convex Programming and Trajectory Optimization,” in IEEE Aerospace Conference, Big Sky, Montana, 2020.

    Abstract: Sequential convex programming (SCP) has recently emerged as an effective tool to quickly compute locally optimal trajectories for robotic and aerospace systems alike, even when initialized with an unfeasible trajectory. In this paper, by focusing on the Guaranteed Sequential Trajectory Optimization (GuSTO) algorithm, we propose a methodology to accelerate SCP-based algorithms through warm-starting. Specifically, leveraging a dataset of expert trajectories from GuSTO, we devise a neural-network-based approach to predict a locally optimal state and control trajectory, which is used to warm-start the SCP algorithm. This approach allows one to retain all the theoretical guarantees of GuSTO while simultaneously taking advantage of the fast execution of the neural network and reducing the time and number of iterations required for GuSTO to converge. The result is a faster and theoretically guaranteed trajectory optimization algorithm.

      author = {Banerjee, S. and Lew, T. and Bonalli, R. and Alfaadhel, A. and Alomar, I. A. and Shageer, H. M. and Pavone, M.},
      title = {Learning-based Warm-Starting for Fast Sequential Convex Programming and Trajectory Optimization},
      booktitle = {{IEEE Aerospace Conference}},
      year = {2020},
      address = {Big Sky, Montana},
      month = mar,
      url = {/wp-content/papercite-data/pdf/Banerjee.Lew.Bonalli.ea.AeroConf20.pdf},
      owner = {lew},
      timestamp = {2020-01-09}