Date: June 29, 2018
Time: 8:55am - 5:00pm
Location: Doherty Hall 2210, CMU, Pittsburgh, USA
We will be using Pigenhole for live and interactive Q&A, go to
and use the following format to ask questions
Recent advances in machine learning techniques from the emergence of deep learning, and access to large amounts of data and powerful computing hardware have led to great strides in the state-of-the-art in robotics and artificial intelligence. In contrast to traditional approaches that are strongly model-based with priors and explicit structural constraints, these newer approaches tend to be data-driven and often neglect the underlying problem structure. As a consequence, while these approaches usually outperform their traditional counterparts on many robotics problems, achieving good generalization, task transfer and data-efficiency has been challenging. Combining the strengths of the two paradigms:
- The flexibility of modern learning and inference techniques, and
- The domain knowledge and structural priors of traditional methods
should help bridge this gap.
The goal of this workshop is to bring together researchers from robotics and machine learning to investigate, at the intersection of the two paradigms, 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.
May 19 May 26 June 7 June 14
Submission deadline (AoE time) Extended submission deadline (AoE time) Notification of acceptance Camera ready deadline
Call for Abstracts
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://easychair.org/conferences/?conf=rss18lair
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.