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GTC ON-DEMAND

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Abstract:
The Toyota Research Institute is going beyond supervised learning for automated driving and exploring problems that affect research and development of long-term, large-scale autonomous robots. These problems include unsupervised domain adaptation, self-supervised learning, and robustness to edge cases. This session will dive into robotics systems, especially end-to-end vs. modular design and human-robot interaction. It will also include some of TRI's related research directions, especially those around world-scale cloud robotics.
The Toyota Research Institute is going beyond supervised learning for automated driving and exploring problems that affect research and development of long-term, large-scale autonomous robots. These problems include unsupervised domain adaptation, self-supervised learning, and robustness to edge cases. This session will dive into robotics systems, especially end-to-end vs. modular design and human-robot interaction. It will also include some of TRI's related research directions, especially those around world-scale cloud robotics.  Back
 
Topics:
Autonomous Vehicles, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9314
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Abstract:
Toyota Research Institute's (TRI) mission is to improve the quality of human life through advances in artificial intelligence, automated driving, and robotics. Learn more about their research and how they are using AWS EC2 P3 instances, industry's most powerful GPUs instances, in combination with other AWS services to enable autonomous vehicles and robots at scale.
Toyota Research Institute's (TRI) mission is to improve the quality of human life through advances in artificial intelligence, automated driving, and robotics. Learn more about their research and how they are using AWS EC2 P3 instances, industry's most powerful GPUs instances, in combination with other AWS services to enable autonomous vehicles and robots at scale.  Back
 
Topics:
Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81014
Streaming:
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