activity
20182022
most citedCan Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

53 citations · 58 across the 6 of their papers we have counts for

collaborators

12 papers

cs.CY20222 cited

Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles

Thomas Krendl Gilbert, Aaron J. Snoswell, Michael Dennis +2

Autonomous Vehicles (AVs) will have a transformative impact on society. Beyond the local safety and efficiency of individual vehicles, these effects will also change how people int…

cs.LG20221 cited

Control-Aware Prediction Objectives for Autonomous Driving

Rowan McAllister, Blake Wulfe, Jean Mercat +3

Autonomous vehicle software is typically structured as a modular pipeline of individual components (e.g., perception, prediction, and planning) to help separate concerns into inter…

cs.LG20222 cited

Dynamics-Aware Comparison of Learned Reward Functions

Blake Wulfe, Ashwin Balakrishna, Logan Ellis +3

The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world. However, comparing reward functions, for example a…

cs.RO2021

Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models

Nicholas Rhinehart, Jeff He, Charles Packer +4

Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents. Consider driving…

cs.LG202053 cited

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

Angelos Filos, Panagiotis Tigas, Rowan McAllister +3

Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In pr…

cs.LG2020

Learning Invariant Representations for Reinforcement Learning without Reconstruction

Amy Zhang, Rowan McAllister, Roberto Calandra +2

We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstructio…