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Workshop
Robot Learning: Control and Interaction in the Real World
Roberto Calandra · Markus Wulfmeier · Kate Rakelly · Sanket Kamthe · Danica Kragic · Stefan Schaal · Markus Wulfmeier

Sat Dec 14 08:00 AM -- 06:00 PM (PST) @ West 220 - 222
Event URL: http://www.robot-learning.ml »

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.
Introduction and opening remarks (Introduction)
Sat 9:15 a.m. - 9:40 a.m.
Invited Talk - Marc Deisenroth (Talk)
Marc Deisenroth
Sat 9:45 a.m. - 10:30 a.m.
Coffee Break (Break)
Sat 10:30 a.m. - 11:15 a.m.

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/
Sat 11:15 a.m. - 11:30 a.m.

AVID: Translating Human Demonstrations for Automated Learning

 Laura Smith
Sat 11:30 a.m. - 12:00 p.m.
Invited Talk - Takayuki Osa (Talk)
Takayuki Osa
Sat 12:00 p.m. - 1:30 p.m.
Lunch Break (Break)
Sat 1:30 p.m. - 2:00 p.m.
Invited Talk - Raia Hadsell (Talk)
Raia Hadsell
Sat 2:00 p.m. - 2:30 p.m.
Invited Talk - Nima Fazeli (Talk)
Nima Fazeli
Sat 2:30 p.m. - 3:30 p.m.

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/
Sat 3:30 p.m. - 4:00 p.m.
Coffee Break (Break)
Sat 4:00 p.m. - 4:15 p.m.

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.
Invited Talk - Angela Schoellig (Talk)
Angela Schoellig
Sat 4:45 p.m. - 5:15 p.m.
Invited Talk - Edward Johns (Talk)
Edward Johns
Sat 5:15 p.m. - 6:00 p.m.
Panel (Discussion Panel)

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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