Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Jain, Ashesh"'
Autor:
Vitelli, Matt, Chang, Yan, Ye, Yawei, Wołczyk, Maciej, Osiński, Błażej, Niendorf, Moritz, Grimmett, Hugo, Huang, Qiangui, Jain, Ashesh, Ondruska, Peter
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 environments. Current industry-standard solutions use rule-based systems for pla
Externí odkaz:
http://arxiv.org/abs/2109.13602
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. In this paper, we study the history, composition, and dev
Externí odkaz:
http://arxiv.org/abs/2107.08142
Autor:
Houston, John, Zuidhof, Guido, Bergamini, Luca, Ye, Yawei, Chen, Long, Jain, Ashesh, Omari, Sammy, Iglovikov, Vladimir, Ondruska, Peter
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 was collected by a fleet of 20 autonomous vehicles along a fixed route
Externí odkaz:
http://arxiv.org/abs/2006.14480
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 or hold sensor and dataset-specific assumptions, PointFusion is conce
Externí odkaz:
http://arxiv.org/abs/1711.10871
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 intricate than simple geometric constraints on trajectories; they are rather
Externí odkaz:
http://arxiv.org/abs/1601.00741
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 dangerous maneuver. However, many accidents are unavoidable because by the ti
Externí odkaz:
http://arxiv.org/abs/1601.00740
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 computer vision inherently have an underlying high-level structure an
Externí odkaz:
http://arxiv.org/abs/1511.05298
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 anticipation in sensory-rich robotics applications. We introduce a sensor
Externí odkaz:
http://arxiv.org/abs/1509.05016
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 dangerous maneuver. However, many accidents are unavoidable because by the ti
Externí odkaz:
http://arxiv.org/abs/1504.02789
Autor:
Saxena, Ashutosh, Jain, Ashesh, Sener, Ozan, Jami, Aditya, Misra, Dipendra K., Koppula, Hema S.
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 the challenge of dealing with multiple data modalities including symb
Externí odkaz:
http://arxiv.org/abs/1412.0691