10 citations · 10 across the 3 of their papers we have counts for
7 papers
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications
Somrita Banerjee, Apoorva Sharma, Edward Schmerling +3
As input distributions evolve over a mission lifetime, maintaining performance of learning-based models becomes challenging. This paper presents a framework to incrementally retrai…
Particle MPC for Uncertain and Learning-Based Control
Robert Dyro, James Harrison, Apoorva Sharma +1
As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essent…
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks
Apoorva Sharma, Navid Azizan, Marco Pavone
In order to safely deploy Deep Neural Networks (DNNs) within the perception pipelines of real-time decision making systems, there is a need for safeguards that can detect out-of-tr…
Continuous Meta-Learning without Tasks
James Harrison, Apoorva Sharma, Chelsea Finn +1
Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks. However, the meta-learning literature th…
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Sandeep Chinchali, Apoorva Sharma, James Harrison +6
Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and obje…
Robust and Adaptive Planning under Model Uncertainty
Apoorva Sharma, James Harrison, Matthew Tsao +1
Planning under model uncertainty is a fundamental problem across many applications of decision making and learning. In this paper, we propose the Robust Adaptive Monte Carlo Planni…