205 citations · 548 across the 10 of their papers we have counts for
6 papers · 1 filter
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
Matt Vitelli, Yan Chang, Yawei Ye +7
In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environmen…
Autonomy 2.0: Why is self-driving always 5 years away?
Ashesh Jain, Luca Del Pero, Hugo Grimmett +1
Despite the numerous successes of machine learning over the past decade (image recognition, decision-making, NLP, image synthesis), self-driving technology has not yet followed the…
Learning Preferences for Manipulation Tasks from Online Coactive Feedback
Ashesh Jain, Shikhar Sharma, Thorsten Joachims +1
We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intric…
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
Ashesh Jain, Hema S Koppula, Shane Soh +3
Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangero…
PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback
Ashesh Jain, Debarghya Das, Jayesh K Gupta +1
We consider the problem of learning user preferences over robot trajectories for environments rich in objects and humans. This is challenging because the criterion defining a good…
Learning Trajectory Preferences for Manipulators via Iterative Improvement
Ashesh Jain, Brian Wojcik, Thorsten Joachims +1
We consider the problem of learning good trajectories for manipulation tasks. This is challenging because the criterion defining a good trajectory varies with users, tasks and envi…