Rowan McAllister | DeepAI

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∙ 01/27/2023

In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States

Learning-based control approaches have shown great promise in performing...

11 Fernando Castañeda, et al. ∙

∙ 10/04/2022

RAP: Risk-Aware Prediction for Robust Planning

Robust planning in interactive scenarios requires predicting the uncerta...

11 Haruki Nishimura, et al. ∙

∙ 05/15/2022

Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles

Autonomous Vehicles (AVs) will have a transformative impact on society. ...

0 Thomas Krendl Gilbert, et al. ∙

∙ 04/28/2022

Control-Aware Prediction Objectives for Autonomous Driving

Autonomous vehicle software is typically structured as a modular pipelin...

7 Rowan McAllister, et al. ∙

∙ 01/25/2022

Dynamics-Aware Comparison of Learned Reward Functions

The ability to learn reward functions plays an important role in enablin...

0 Blake Wulfe, et al. ∙

∙ 04/26/2021

Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty

Reasoning about the future behavior of other agents is critical to safe ...

3 Boris Ivanovic, et al. ∙

∙ 04/21/2021

Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models

Humans have a remarkable ability to make decisions by accurately reasoni...

0 Nicholas Rhinehart, et al. ∙

∙ 04/20/2021

Outcome-Driven Reinforcement Learning via Variational Inference

While reinforcement learning algorithms provide automated acquisition of...

0 Tim G. J. Rudner, et al. ∙

∙ 06/26/2020

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Out-of-training-distribution (OOD) scenarios are a common challenge of l...

14 Angelos Filos, et al. ∙

∙ 06/18/2020

Learning Invariant Representations for Reinforcement Learning without Reconstruction

We study how representation learning can accelerate reinforcement learni...

9 Amy Zhang, et al. ∙

∙ 04/23/2020

Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads

Transporting suspended payloads is challenging for autonomous aerial veh...

1 Suneel Belkhale, et al. ∙

∙ 05/31/2019

Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations

Reinforcement learning (RL) for robotics is challenging due to the diffi...

19 Brijen Thananjeyan, et al. ∙

∙ 05/03/2019

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

For autonomous vehicles (AVs) to behave appropriately on roads populated...

0 Nicholas Rhinehart, et al. ∙

∙ 12/27/2018

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

Deep learning provides a powerful tool for machine perception when the o...

10 Rowan McAllister, et al. ∙

∙ 10/15/2018

Deep Imitative Models for Flexible Inference, Planning, and Control

Imitation learning provides an appealing framework for autonomous contro...

0 Nicholas Rhinehart, et al. ∙

∙ 05/30/2018

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

Model-based reinforcement learning (RL) algorithms can attain excellent ...

0 Kurtland Chua, et al. ∙

∙ 02/08/2016

Data-Efficient Reinforcement Learning in Continuous-State POMDPs

We present a data-efficient reinforcement learning algorithm resistant t...

0 Rowan McAllister, et al. ∙

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