Demonstration
Deep Robotic Learning using Visual Imagination and Meta-Learning
Chelsea Finn · Frederik Ebert · Tianhe Yu · Annie Xie · Sudeep Dasari · Pieter Abbeel · Sergey Levine

Tue Dec 5th 07:00 -- 10:30 PM @ Pacific Ballroom Concourse #D6

A key, unsolved challenge for learning with real robotic systems is the ability to acquire vision-based behaviors from raw RGB images that can generalize to new objects and new goals. We present two approaches to this goal that we plan to demonstrate: first, learning task-agnostic visual models for planning, which can generalize to new objects and goals, and second, learning to quickly adapt to new objects and environments using meta-imitation learning. In essence, these two approaches seek to generalize and dynamically adapt to new settings, respectively, as we discuss next.

Author Information

Chelsea Finn (UC Berkeley)
Frederik Ebert (UC Berkeley)
Tianhe Yu (Stanford University)
Annie Xie (University of California, Berkeley)
Sudeep Dasari (UC Berkeley)
Pieter Abbeel (UC Berkeley | Gradescope | Covariant)

Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Director of the Berkeley AI Research (BAIR) Lab, Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse venture fund, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Pieter's work is frequently featured in the popular press, including New York Times, BBC, Bloomberg, Wall Street Journal, Wired, Forbes, Tech Review, NPR.

Sergey Levine (UC Berkeley)

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