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The growing capabilities of learning-based methods in control and robotics has precipitated a shift in the design of software for autonomous systems. Recent successes fuel the hope that robots will increasingly perform varying tasks working alongside humans in complex, dynamic environments. However, the application of learning approaches to real-world robotic systems has been limited because real-world scenarios introduce challenges that do not arise in simulation.
In this workshop, we aim to identify and tackle the main challenges to learning on real robotic systems. First, most machine learning methods rely on large quantities of labeled data. While raw sensor data is available at high rates, the required variety is hard to obtain and the human effort to annotate or design reward functions is an even larger burden. Second, algorithms must guarantee some measure of safety and robustness to be deployed in real systems that interact with property and people. Instantaneous reset mechanisms, as common in simulation to recover from even critical failures, present a great challenge to real robots. Third, the real world is significantly more complex and varied than curated datasets and simulations. Successful approaches must scale to this complexity and be able to adapt to novel situations.
Sat 9:00 a.m. - 9:15 a.m.
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Introduction and opening remarks
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Introduction
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Sat 9:15 a.m. - 9:40 a.m.
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Invited Talk - Marc Deisenroth
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Talk
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Marc Deisenroth 🔗 |
Sat 9:45 a.m. - 10:30 a.m.
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Coffee Break
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Sat 10:30 a.m. - 11:15 a.m.
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Posters 1
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Poster session
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All poster presenters are welcome to present at both poster sessions. Please see the list of accepted papers at our website: http://www.robot-learning.ml/2019/ |
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Sat 11:15 a.m. - 11:30 a.m.
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Contributed Talk - Laura Smith
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Talk
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AVID: Translating Human Demonstrations for Automated Learning |
Laura Smith 🔗 |
Sat 11:30 a.m. - 12:00 p.m.
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Invited Talk - Takayuki Osa
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Talk
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Takayuki Osa 🔗 |
Sat 12:00 p.m. - 1:30 p.m.
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Lunch Break
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Sat 1:30 p.m. - 2:00 p.m.
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Invited Talk - Raia Hadsell
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Talk
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Raia Hadsell 🔗 |
Sat 2:00 p.m. - 2:30 p.m.
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Invited Talk - Nima Fazeli
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Talk
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Nima Fazeli 🔗 |
Sat 2:30 p.m. - 3:30 p.m.
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Posters 2
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Poster Session
)
All poster presenters are welcome to present at both poster sessions. Please see the list of accepted papers at our website: http://www.robot-learning.ml/2019/ |
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Sat 3:30 p.m. - 4:00 p.m.
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Coffee Break
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Sat 4:00 p.m. - 4:15 p.m.
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Contributed Talk (Best Paper) - Michelle Lee & Carlos Florensa
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Talk
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Combining Model-Free and Model-Based Strategies for Sample-Efficient Reinforcement Learning |
Carlos Florensa · Michelle A. Lee 🔗 |
Sat 4:15 p.m. - 4:45 p.m.
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Invited Talk - Angela Schoellig
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Talk
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Angela Schoellig 🔗 |
Sat 4:45 p.m. - 5:15 p.m.
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Invited Talk - Edward Johns
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Talk
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Edward Johns 🔗 |
Sat 5:15 p.m. - 6:00 p.m.
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Panel
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Discussion Panel
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Author Information
Roberto Calandra (Facebook AI Research)
Markus Wulfmeier (DeepMind)
Kate Rakelly (UC Berkeley)
Sanket Kamthe (Imperial College London)
Danica Kragic (KTH Royal Institute of Technology)
Stefan Schaal (Google)
Markus Wulfmeier (DeepMind)
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