no code implementations • 28 Sep 2021 • Matt Vitelli, Yan Chang, Yawei Ye, Maciej Wołczyk, Błażej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, Peter Ondruska
To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).
no code implementations • 16 Jul 2021 • Ashesh Jain, Luca Del Pero, Hugo Grimmett, Peter Ondruska
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 same trend.
3 code implementations • 25 Jun 2020 • John Houston, Guido Zuidhof, Luca Bergamini, Yawei Ye, Long Chen, Ashesh Jain, Sammy Omari, Vladimir Iglovikov, Peter Ondruska
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.
3 code implementations • CVPR 2018 • Danfei Xu, Dragomir Anguelov, Ashesh Jain
We present PointFusion, a generic 3D object detection method that leverages both image and 3D point cloud information.
no code implementations • 5 Jan 2016 • Ashesh Jain, Hema S. Koppula, Shane Soh, Bharad Raghavan, Avi Singh, Ashutosh Saxena
We introduce a diverse data set with 1180 miles of natural freeway and city driving, and show that we can anticipate maneuvers 3. 5 seconds before they occur in real-time with a precision and recall of 90. 5\% and 87. 4\% respectively.
no code implementations • 5 Jan 2016 • Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena
We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots.
2 code implementations • CVPR 2016 • Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena
The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps.
Ranked #4 on Skeleton Based Action Recognition on CAD-120
no code implementations • 16 Sep 2015 • Ashesh Jain, Avi Singh, Hema S. Koppula, Shane Soh, Ashutosh Saxena
We introduce a sensory-fusion architecture which jointly learns to anticipate and fuse information from multiple sensory streams.
no code implementations • ICCV 2015 • Ashesh Jain, Hema S. Koppula, Bharad Raghavan, Shane Soh, Ashutosh Saxena
We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3. 5 seconds before they occur with over 80\% F1-score in real-time.
no code implementations • 1 Dec 2014 • Ashutosh Saxena, Ashesh Jain, Ozan Sener, Aditya Jami, Dipendra K. Misra, Hema S. Koppula
In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks.
no code implementations • 10 Jun 2014 • Ashesh Jain, Debarghya Das, Jayesh K. Gupta, Ashutosh Saxena
We represent trajectory preferences using a cost function that the robot learns and uses it to generate good trajectories in new environments.
no code implementations • NeurIPS 2013 • Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena
In this paper, we propose a co-active online learning framework for teaching robots the preferences of its users for object manipulation tasks.