Workshop
3rd Robot Learning Workshop
Masha Itkina · Alex Bewley · Roberto Calandra · Igor Gilitschenski · Julien PEREZ · Ransalu Senanayake · Markus Wulfmeier · Vincent Vanhoucke
Fri 11 Dec, 7:30 a.m. PST
In the proposed workshop, we aim to discuss the challenges and opportunities for machine learning research in the context of physical systems. This discussion involves the presentation of recent methods and the experiences made during the deployment on real-world platforms. Such deployment requires a significant degree of generalization. Namely, the real world is vastly more complex and diverse compared to fixed curated datasets and simulations. Deployed machine learning models must scale to this complexity, be able to adapt to novel situations, and recover from mistakes. Moreover, the workshop aims to strengthen further the ties between the robotics and machine learning communities by discussing how their respective recent directions result in new challenges, requirements, and opportunities for future research.
Following the success of previous robot learning workshops at NeurIPS, the goal of this workshop is to bring together a diverse set of scientists at various stages of their careers and foster interdisciplinary communication and discussion.
In contrast to the previous robot learning workshops which focused on applications in robotics for machine learning, this workshop extends the discussion on how real-world applications within the context of robotics can trigger various impactful directions for the development of machine learning. For a more engaging workshop, we encourage each of our senior presenters to share their presentations with a PhD student or postdoctoral researcher from their lab. Additionally, all our presenters - invited and contributed - are asked to add a ``dirty laundry’’ slide, describing the limitations and shortcomings of their work. We expect this will aid further discussion in poster and panel sessions in addition to helping junior researchers avoid similar roadblocks along their path.
Schedule
Fri 7:30 a.m. - 7:45 a.m.
|
Introduction
(
Introduction
)
>
|
Masha Itkina 🔗 |
Fri 7:45 a.m. - 8:30 a.m.
|
Invited Talk - "Walking the Boundary of Learning and Interaction"
(
Invited Talk
)
>
SlidesLive Video |
Dorsa Sadigh · Erdem Biyik 🔗 |
Fri 8:31 a.m. - 8:45 a.m.
|
Contributed Talk 1 - "Accelerating Reinforcement Learning with Learned Skill Priors" (Best Paper Runner-Up)
(
Contributed Talk
)
>
SlidesLive Video |
Karl Pertsch 🔗 |
Fri 8:45 a.m. - 9:45 a.m.
|
Poster Session 1 ( Poster Session ) > link | 🔗 |
Fri 9:46 a.m. - 10:30 a.m.
|
Invited Talk - "Object- and Action-Centric Representational Robot Learning"
(
Invited Talk
)
>
SlidesLive Video |
Pete Florence · Daniel Seita 🔗 |
Fri 10:31 a.m. - 11:15 a.m.
|
Invited Talk - "State of Robotics @ Google"
(
Invited Talk
)
>
SlidesLive Video |
Carolina Parada 🔗 |
Fri 11:15 a.m. - 3:00 p.m.
|
Break
|
🔗 |
Fri 3:00 p.m. - 4:00 p.m.
|
Discussion Panel
(
Discussion Panel
)
>
|
Pete Florence · Dorsa Sadigh · Carolina Parada · Jeannette Bohg · Roberto Calandra · Peter Stone · Fabio Ramos 🔗 |
Fri 4:01 p.m. - 4:45 p.m.
|
Invited Talk - "Learning-based Control of a Legged Robot"
(
Invited Talk
)
>
|
Jemin Hwangbo · JooWoong Byun 🔗 |
Fri 4:46 p.m. - 5:00 p.m.
|
Contributed Talk 2 - "Multi-Robot Deep Reinforcement Learning via Hierarchically Integrated Models" (Best Paper)
(
Contributed Talk
)
>
SlidesLive Video |
Yijun Kang 🔗 |
Fri 5:00 p.m. - 5:30 p.m.
|
Break
|
🔗 |
Fri 6:15 p.m. - 7:15 p.m.
|
Poster Session 2 ( Poster Session ) > link | 🔗 |
Fri 7:15 p.m. - 7:30 p.m.
|
Closing
(
Closing
)
>
|
🔗 |