205 citations · 548 across the 10 of their papers we have counts for
12 papers
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…
One Thousand and One Hours: Self-driving Motion Prediction Dataset
John Houston, Guido Zuidhof, Luca Bergamini +6
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1,000 hours of data. This w…
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
Danfei Xu, Dragomir Anguelov, Ashesh Jain
We present PointFusion, a generic 3D object detection method that leverages both image and 3D point cloud information. Unlike existing methods that either use multi-stage pipelines…
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…
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Ashesh Jain, Amir R. Zamir, Silvio Savarese +1
Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in…
Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture
Ashesh Jain, Avi Singh, Hema S Koppula +2
Anticipating the future actions of a human is a widely studied problem in robotics that requires spatio-temporal reasoning. In this work we propose a deep learning approach for ant…
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Ashesh Jain, Hema S. Koppula, Bharad Raghavan +2
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…
RoboBrain: Large-Scale Knowledge Engine for Robots
Ashutosh Saxena, Ashesh Jain, Ozan Sener +3
In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks. Building such an engine brings with it…
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…