Search Results for author: Ashesh Jain

Found 12 papers, 3 papers with code

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies

no code implementations28 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).

Imitation Learning

Autonomy 2.0: Why is self-driving always 5 years away?

no code implementations16 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.

Decision Making Image Generation

One Thousand and One Hours: Self-driving Motion Prediction Dataset

3 code implementations25 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.

Autonomous Vehicles Motion Forecasting +2

Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture

no code implementations5 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.

Learning Preferences for Manipulation Tasks from Online Coactive Feedback

no code implementations5 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.

Structural-RNN: Deep Learning on Spatio-Temporal Graphs

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.

Human Pose Forecasting Skeleton Based Action Recognition

Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture

no code implementations16 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.

Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

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.

RoboBrain: Large-Scale Knowledge Engine for Robots

no code implementations1 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.

PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback

no code implementations10 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.


Learning Trajectory Preferences for Manipulators via Iterative Improvement

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.

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