no code implementations • 18 May 2024 • Jay Patrikar, Sushant Veer, Apoorva Sharma, Marco Pavone, Sebastian Scherer
Modern motion planners for autonomous driving frequently use imitation learning (IL) to draw from expert driving logs.
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.
no code implementations • 3 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.
no code implementations • 28 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.
1 code implementation • 4 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.
1 code implementation • 14 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.
no code implementations • 14 Dec 2021 • Nikhil Patel, James Hale, Kanika Jindal, Apoorva Sharma, Yichun Yu
We propose to take on the problem ofWord Sense Disambiguation (WSD).
no code implementations • 11 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.
no code implementations • 6 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.
2 code implementations • 24 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.
no code implementations • 26 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.
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.
no code implementations • 15 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?
no code implementations • 9 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.
3 code implementations • 24 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.
1 code implementation • 16 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.