The growing capabilities of learning-based methods in control and robotics have 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 not arising in simulation.

In this workshop, we aim to identify and tackle the main challenges to learning on real robotic systems. First, many current 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, be able to adapt to novel situations and recover from mistakes.

As a community, we are exploring a wide range of solutions to each of these challenges. To explore the limits of different directions, we aim to address in particular questions about the trade-offs and potential necessity of particular design aspects via included panel discussion as well as the invited presentations:

The primary focus of the submission lies on tackling these challenges resulting from operation in the real world. We will encourage submissions that experiment on physical systems, and specifically consider algorithmic developments aimed at tackling the challenges presented by physical systems. We believe this focus on real-world application will bring together a cross-section of researchers working on different areas of research for a fruitful exchange of ideas including our invited speakers.

Important dates

Invited Speakers

Organizers

Schedule

09:00 Introduction and opening remarks
09:15 Invited talk - Marc Deisenroth
09:45 Coffee break
10:30 Poster session 1
11:15 Contributed talk - Laura Smith presenting AVID: Translating Human Demonstrations for Automated Learning
11:30 Invited talk - Takayuki Osa
12:00 Lunch break
13:30 Invited talk - Raia Hadsell
14:00 Invited talk - Nima Fazeli
14:30 Poster session 2
15:30 Coffee break
16:00 Contributed talk - Michelle Lee and Carlos Florensa presenting Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
16:15 Invited talk - Angela Schoellig
16:45 Invited talk - Edward Johns
17:15 Panel discussion
18:00 End

Accepted Papers

Accepted papers are listed in alphabetical order. All papers will be presented in poster format during both poster sessions.

Program Committee

We would like to thank the program committee for shaping the excellent technical program. In alphabetical order they are:

Abbas Abdolmaleki, Hany Abdulsamad, Andrea Bajcsy, Feryal Behbahani, Djalel Benbouzid, Michael Bloesch, Caterina Buizza, Roberto Calandra, Nutan Chen, Misha Denil, Coline Devin, Marco Ewerton, Walter Goodwin, Tuomas Haarnoja, Roland Hafner, James Harrison, Karol Hausman, Edward Johns, Ashvin Nair, Takayuki Osa, Simone Parisi, Akshara Rai, Nemanja Rakicevic, Dushyant Rao, Siddharth Reddy, Apoorva Sharma, Johannes A. Stork, Li Sun, Filipe Veiga, Ruohan Wang, Ruohan Wang, Rob Weston, Yizhe Wu

Contacts

For any further questions, you can contact us at neuripswrl2019@robot-learning.ml

Sponsors

We are very thankful to our corporate sponsors for enabling us to provide student travel grants!