10 citations · 32 across the 22 of their papers we have counts for
25 papers
Coverage Aware Active Evaluation for Failure Discovery with Paired Systems
Anjali Parashar, Rachel Luo, Apoorva Sharma +6
Autonomous systems can fail in rare and heterogeneous ways, making real-world failure discovery difficult under limited testing budgets. Although cheaper proxies such as simulators…
Litmus: Zero-Label, Code-Driven Metric Specification for Evaluating AI Systems
Prajjwal Gupta, Prasang Gupta, Vishal Bhutani +4
As agentic LLM systems move from prototypes to deployment, evaluating them has become more important and more difficult. Evaluation goals are often left implicit. Without a clear a…
StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement
Junwon Seo, Sushant Veer, Ran Tian +6
Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot actions. While WMs can model d…
Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning
Zhenghao "Mark" Peng, Wenhao Ding, Yurong You +11
Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet…
The Case for Negative Data: From Crash Reports to Counterfactuals for Reasonable Driving
Jay Patrikar, Apoorva Sharma, Sushant Veer +3
Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisel…
Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators
Kaustav Chakraborty, Zeyuan Feng, Sushant Veer +6
The advent of end-to-end autonomy stacks - often lacking interpretable intermediate modules - has placed an increased burden on ensuring that the final output, i.e., the motion pla…
Sim2Val: Leveraging Correlation Across Test Platforms for Variance-Reduced Metric Estimation
Rachel Luo, Heng Yang, Michael Watson +4
Learning-based robotic systems demand rigorous validation to assure reliable performance, but extensive real-world testing is often prohibitively expensive, and if conducted may st…
Online Aggregation of Trajectory Predictors
Alex Tong, Apoorva Sharma, Sushant Veer +2
Trajectory prediction, the task of forecasting future agent behavior from past data, is central to safe and efficient autonomous driving. A diverse set of methods (e.g., rule-based…
STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes
Jiawei Yang, Jiahui Huang, Yuxiao Chen +10
We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods often…
System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles
Kaustav Chakraborty, Zeyuan Feng, Sushant Veer +4
The safety-critical nature of autonomous vehicle (AV) operation necessitates development of task-relevant algorithms that can reason about safety at the system level and not just a…
RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
Jay Patrikar, Sushant Veer, Apoorva Sharma +2
Modern motion planners for autonomous driving frequently use imitation learning (IL) to draw from expert driving logs. Although IL benefits from its ability to glean nuanced and mu…
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
Apoorva Sharma, Sushant Veer, Asher Hancock +3
Inductive Conformal Prediction (ICP) provides a practical and effective approach for equipping deep learning models with uncertainty estimates in the form of set-valued predictions…
Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors
Sushant Veer, Apoorva Sharma, Marco Pavone
Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learnin…
A System-Level View on Out-of-Distribution Data in Robotics
Rohan Sinha, Apoorva Sharma, Somrita Banerjee +6
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…
Tractable Uncertainty-Aware Meta-Learning
Young-Jin Park, Cesar Almecija, Apoorva Sharma +1
Meta-learning is a popular approach for learning new tasks with limited data by leveraging the commonalities among different tasks. However, meta-learned models can perform poorly…
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…
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
Thomas Lew, Apoorva Sharma, James Harrison +2
We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation. Specifically, we disc…
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…
Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework
Thomas Lew, Apoorva Sharma, James Harrison +2
Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this work, w…
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…
Meta-Learning Priors for Efficient Online Bayesian Regression
James Harrison, Apoorva Sharma, Marco Pavone
Gaussian Process (GP) regression has seen widespread use in robotics due to its generality, simplicity of use, and the utility of Bayesian predictions. The predominant implementati…