[robotics-worldwide] [meetings] 2nd CfP: RSS 2018 Workshop on Learning and Inference in Robotics: Integrating Structure, Priors and Models

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[robotics-worldwide] [meetings] 2nd CfP: RSS 2018 Workshop on Learning and Inference in Robotics: Integrating Structure, Priors and Models

Mustafa Mukadam
Call for Abstracts

RSS 2018 Workshop on
Learning and Inference in Robotics: Integrating Structure, Priors and Models

Location: Pittsburgh, Pennsylvania, USA
Date: June 29, 2018
URL: https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_view_rss2018lair&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=jXubcmIyhPHFbM7QQy68n70sjxSrPuIXIo8Ew8IDvcw&s=iyQMfMO6e-q-lZEITbjwtEcZzwcM78TMVy6IctcO76g&e=

Important Dates
May 19 - Submission deadline (AoE time)
June 2 - Notification of acceptance
June 9 - Camera ready deadline
June 29 - Workshop

The goal of this workshop is to bring together researchers from robotics
and machine learning to investigate, techniques for structured learning and
inference. Our notion of "structure" is very general. In the context of
robot learning and inference this manifests in many ways: as a specific
architecture of a (probabilistic) graphical model or (deep) network, an
intermediate representation, a loss function, and so on. Some of the
questions we hope to answer include:
- How can we leverage structure to improve the state of the art in
learning/inference models?
- What is the right mix between structure/priors/models and learning?
- How can we establish benchmarks/baselines that show the effectiveness of
using structure in learning/inference for robotics?
A special emphasis will be on methods that tightly integrate structure with
learning and are demonstrably applicable in the real-world, particularly on
problems like autonomous navigation, manipulation, and field robotics.

We solicit up to 3 pages extended abstracts (excluding citations)
conforming to the official RSS style guidelines. Submissions can include
archived or previously accepted work (please make a note of this in the
submission). Reviewing will be single blind.

Submission link: https://urldefense.proofpoint.com/v2/url?u=https-3A__easychair.org_conferences_-3Fconf-3Drss18lair&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=jXubcmIyhPHFbM7QQy68n70sjxSrPuIXIo8Ew8IDvcw&s=A8ktv55Bb-9lDcfnqWp8Rdwoc0ZJjYDt23mjR-S2wBs&e=

Topics of interest include, but are not limited to:
- Structured inference and learning for robotics
- Deep learning with structure and priors
- Learning structured representations for perception, planning and control
- Integrating learning and model-based robotics
- Structured losses and semi/self-supervised learning
- Reinforcement/Imitation learning using domain knowledge
- Autonomous navigation, mobile manipulation with structured learning
- Structured optimization with deep learning and automatic differentiation
- Deep learning with graphical models

All accepted contributions will be presented in interactive poster
sessions. A subset of accepted contributions will be featured in the
workshop as spotlight presentations.

Mustafa Mukadam (GaTech)
Arunkumar Byravan (UW)
Byron Boots (GaTech)

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