Somrita Banerjee
YOU?
Author Swipe
View article: Deep Learning Warm Starts for Trajectory Optimization on the International Space Station
Deep Learning Warm Starts for Trajectory Optimization on the International Space Station Open
Trajectory optimization is a cornerstone of modern robot autonomy, enabling systems to compute trajectories and controls in real-time while respecting safety and physical constraints. However, it has seen limited usage in spaceflight appli…
View article: Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications
Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications Open
Foundation Models (FMs), e.g., large language models, possess attributes of intelligence which offer promise to endow a robot with the contextual understanding necessary to navigate complex, unstructured tasks in the wild. We see three cor…
View article: Diagnostic Runtime Monitoring with Martingales
Diagnostic Runtime Monitoring with Martingales Open
Machine learning systems deployed in safety-critical robotics settings must be robust to distribution shifts. However, system designers must understand the cause of a distribution shift in order to implement the appropriate intervention or…
View article: Contingency Planning Using Bi-level Markov Decision Processes for Space Missions
Contingency Planning Using Bi-level Markov Decision Processes for Space Missions Open
This work focuses on autonomous contingency planning for scientific missions by enabling rapid policy computation from any off-nominal point in the state space in the event of a delay or deviation from the nominal mission plan. Successful …
View article: A System-Level View on Out-of-Distribution Data in Robotics
A System-Level View on Out-of-Distribution Data in Robotics Open
When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an impo…
View article: Learning to Branch-and-Bound to Route an Autonomous Mobility on Demand System
Learning to Branch-and-Bound to Route an Autonomous Mobility on Demand System Open
Autonomous Mobility on Demand (AMoD) is a system consisting of a fleet of centrally-controlled, autonomous vehicles that take customers from their requested origins to their requested destinations. In order to minimize the distance travele…
View article: Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications Open
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, wh…
View article: Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics
Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics Open
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 t…
View article: Learning-based Warm-Starting for Fast Sequential Convex Programming and Trajectory Optimization
Learning-based Warm-Starting for Fast Sequential Convex Programming and Trajectory Optimization Open
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 f…