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GTC ON-DEMAND

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Abstract:
Programming robots remains notoriously difficult. Equipping robots with the ability to learn would bypass the need for what often ends up being time-consuming task-specific programming. This talk will describe recent progress in deep reinforcement learning (robots learning through trial and error), in apprenticeship learning (robots learning from observing people), and in meta-learning for action (robots learning to learn). Our work has led to new robotic capabilities in manipulation, locomotion, and flight, with the same approach underlying advances in each of these domains.
Programming robots remains notoriously difficult. Equipping robots with the ability to learn would bypass the need for what often ends up being time-consuming task-specific programming. This talk will describe recent progress in deep reinforcement learning (robots learning through trial and error), in apprenticeship learning (robots learning from observing people), and in meta-learning for action (robots learning to learn). Our work has led to new robotic capabilities in manipulation, locomotion, and flight, with the same approach underlying advances in each of these domains.  Back
 
Topics:
Intelligent Machines, IoT & Robotics, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9315
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Abstract:

We''ll introduce the latest advances on topics such as learning-to-learn, meta-learning, deep learning for robotics, deep reinforcement learning, and AI for manufacturing and logistics.

We''ll introduce the latest advances on topics such as learning-to-learn, meta-learning, deep learning for robotics, deep reinforcement learning, and AI for manufacturing and logistics.

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Topics:
Advanced AI Learning Techniques, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8118
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Abstract:

Deep learning has enabled significant advances in supervised learning problems such as speech recognition and visual recognition. Reinforcement learning provides only a weaker supervisory signal, posing additional challenges in the form of temporal credit assignment and exploration. Nevertheless, deep reinforcement learning has already enabled learning to play Atari games from raw pixels (without access to the underlying game state) and learning certain types of visuomotor manipulation primitives. I will discuss major challenges for, as well as some preliminary promising results towards, making deep reinforcement learning applicable to real robotic problems.

Deep learning has enabled significant advances in supervised learning problems such as speech recognition and visual recognition. Reinforcement learning provides only a weaker supervisory signal, posing additional challenges in the form of temporal credit assignment and exploration. Nevertheless, deep reinforcement learning has already enabled learning to play Atari games from raw pixels (without access to the underlying game state) and learning certain types of visuomotor manipulation primitives. I will discuss major challenges for, as well as some preliminary promising results towards, making deep reinforcement learning applicable to real robotic problems.

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Topics:
Artificial Intelligence and Deep Learning, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6812
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