Search Results for author: Apoorva Sharma

Found 16 papers, 6 papers with code

PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction

no code implementations NeurIPS 2023 Apoorva Sharma, Sushant Veer, Asher Hancock, Heng Yang, Marco Pavone, Anirudha Majumdar

To remedy this, recent work has proposed learning model and score function parameters using data to directly optimize the efficiency of the ICP prediction sets.

Conformal Prediction Generalization Bounds

Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors

no code implementations3 Jul 2023 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.

Autonomous Vehicles Trajectory Prediction

A System-Level View on Out-of-Distribution Data in Robotics

no code implementations28 Dec 2022 Rohan Sinha, Apoorva Sharma, Somrita Banerjee, Thomas Lew, Rachel Luo, Spencer M. Richards, Yixiao Sun, Edward Schmerling, Marco Pavone

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.

Uncertainty-Aware Meta-Learning for Multimodal Task Distributions

1 code implementation4 Oct 2022 Cesar Almecija, Apoorva Sharma, Navid Azizan

In this work, we present UnLiMiTD (uncertainty-aware meta-learning for multimodal task distributions), a novel method for meta-learning that (1) makes probabilistic predictions on in-distribution tasks efficiently, (2) is capable of detecting OoD context data at test time, and (3) performs on heterogeneous, multimodal task distributions.

Bayesian Inference Few-Shot Learning

Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications

1 code implementation14 Sep 2022 Somrita Banerjee, Apoorva Sharma, Edward Schmerling, Max Spolaor, Michael Nemerouf, Marco Pavone

Algorithms within this framework are evaluated based on (1) model performance throughout mission lifetime and (2) cumulative costs associated with labeling and model retraining.

Active Learning Management +2

On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation

no code implementations11 Nov 2021 Thomas Lew, Apoorva Sharma, James Harrison, Edward Schmerling, Marco Pavone

We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation.

Particle MPC for Uncertain and Learning-Based Control

no code implementations6 Apr 2021 Robert Dyro, James Harrison, Apoorva Sharma, Marco Pavone

As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance.

Model-based Reinforcement Learning Model Predictive Control

Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks

2 code implementations24 Feb 2021 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-training-distribution (OoD) inputs both efficiently and accurately.

Decision Making Out-of-Distribution Detection +1

Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework

no code implementations26 Aug 2020 Thomas Lew, Apoorva Sharma, James Harrison, Andrew Bylard, Marco Pavone

In this work, we propose a practical and theoretically-justified approach to maintaining safety in the presence of dynamics uncertainty.

Meta-Learning Meta Reinforcement Learning

Continuous Meta-Learning without Tasks

2 code implementations NeurIPS 2020 James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone

In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.

Image Classification Meta-Learning +2

Network Offloading Policies for Cloud Robotics: a Learning-based Approach

no code implementations15 Feb 2019 Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone

In this paper, we formulate a novel Robot Offloading Problem --- how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication?

Decision Making object-detection +2

Robust and Adaptive Planning under Model Uncertainty

no code implementations9 Jan 2019 Apoorva Sharma, James Harrison, Matthew Tsao, Marco Pavone

The first, RAMCP-F, converges to an optimal risk-sensitive policy without having to rebuild the search tree as the underlying belief over models is perturbed.

Computational Efficiency Decision Making

Meta-Learning Priors for Efficient Online Bayesian Regression

3 code implementations24 Jul 2018 James Harrison, Apoorva Sharma, Marco Pavone

However, this approach suffers from two main drawbacks: (1) it is computationally inefficient, as computation scales poorly with the number of samples; and (2) it can be data inefficient, as encoding prior knowledge that can aid the model through the choice of kernel and associated hyperparameters is often challenging and unintuitive.

Meta-Learning regression

BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning

1 code implementation16 Jun 2018 Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone

Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance.

Continuous Control reinforcement-learning +2

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