Somrita Banerjee

Contacts:

Email: somrita at stanford dot edu

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.

Awards:

  • Stanford Graduate Fellowship

ASL Publications

  1. S. Banerjee, A. Sharma, E. Schmerling, M. Spolaor, M. Nemerouf, and M. Pavone, “Data Lifecycle Management for Learning-based Aerospace Applications,” in IEEE Aerospace Conference, 2023.

    [BibTeX] [Abstract]

    Abstract: As input distributions evolve over a mission lifetime, maintaining performance of learning-based models becomes challenging. This paper presents a framework to incrementally retrain a model by selecting a subset of test inputs to label, which allows the model to adapt to changing input distributions. Algorithms within this framework are evaluated based on (1) model performance throughout mission lifetime and (2) cumulative costs associated with labeling and model retraining. We provide an open-source benchmark of a satellite pose estimation model trained on images of a satellite in space and deployed in novel scenarios (e.g., different backgrounds or misbehaving pixels), where algorithms are evaluated on their ability to maintain high performance by retraining on a subset of inputs. We also propose a novel algorithm to select a diverse subset of inputs for labeling, by characterizing the information gain from an input using Bayesian uncertainty quantification and choosing a subset that maximizes collective information gain using concepts from batch active learning. We show that our algorithm outperforms others on the benchmark, e.g., achieves comparable performance to an algorithm that labels 100% of inputs, while only labeling 50% of inputs, resulting in low costs and high performance over the mission lifetime.

     
    @inproceedings{BanerjeeSharmaEtAl2022,
  author = {Banerjee, S. and Sharma, A. and Schmerling, E. and Spolaor, M. and Nemerouf, M. and Pavone, M.},
  title = {Data Lifecycle Management for Learning-based Aerospace Applications},
  booktitle = {{IEEE Aerospace Conference}},
  year = {2023},
  url = {https://arxiv.org/abs/2209.06855},
  owner = {somrita},
  timestamp = {2022-09-14}
}
  2. R. Sinha, S. Sharma, S. Banerjee, T. Lew, R. Luo, S. M. Richards, Y. Sun, E. Schmerling, and M. Pavone, “A System-Level View on Out-of-Distribution Data in Robotics,” 2022. (Submitted)

    [BibTeX] [Abstract]

    Abstract: When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.

     
    @inproceedings{SinhaSharmaEtAl2022,
  author = {Sinha, R. and Sharma, S. and Banerjee, S. and Lew, T. and Luo, R. and Richards, S. M. and Sun, Y. and Schmerling, E. and Pavone, M.},
  title = {A System-Level View on Out-of-Distribution Data in Robotics},
  year = {2022},
  keywords = {sub},
  url = {https://arxiv.org/abs/2212.14020},
  owner = {rhnsinha},
  timestamp = {2022-12-30}
}
  3. 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.

    [BibTeX] [Abstract]

    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.

     
    @inproceedings{BanerjeeHarrisonEtAl2020,
  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 = {https://arxiv.org/abs/2009.10191},
  owner = {somrita},
  timestamp = {2020-09-18}
}
  4. 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.

    [BibTeX] [Abstract]

    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.

     
    @inproceedings{BanerjeeEtAl2020,
  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}
}