This workshop aims to facilitate discussion about key issues related to scalable and generalizable robot learning, with a focus on three main axes: state/action representations, abstractions, and behavior priors. While robot learning holds the promise of endowing robots with complex skills, in practice, the scalability and generalization of skills are still an open problem. One pressing issue is the type and quality of representations that we could learn, for e.g., when learning in simulation or when learning in a different domain. Moreover, the sample-efficient learning of modular skills is crucial for robotics, and in this regard, the use of priors, either classical or learned, can greatly benefit learning. This makes researchers think about the best way to induce prior knowledge into a learning system, but the representation of such priors should “fit” the follow-up tasks. Moreover, the decomposition of complex long-horizon tasks into a sequence of simpler ones is a well-known strategy in robot planning. However, the type and levels of abstractions needed are open questions, and their representation and connection to observations are still challenging. 

Workshop panels will discuss fundamental open research questions, such as:

• How can we effectively represent multisensorial data and experiences across different instances of the robot’s operating life?

• How can we take advantage of robotic priors, scene structure, and demonstrations to accelerate robotic learning?

• What and how many levels of abstractions should we consider for long-horizon robotic tasks?

• How can we achieve effective lifelong robot learning, while avoiding catastrophic forgetting?

This workshop will bring together a cohort of top researchers, both early-career and established ones, to discuss these highly relevant problems, and open up new directions for developing systems and algorithms for robot learning that respect top-down and bottom-up compatibility (in terms of representations, abstractions, and sequencing strategies). After the workshop, we plan to organize a special issue at a major robotics journal (e.g., IEEE RA-L) on the topic of representations, abstractions, and priors for robot learning, also driven by the outcomes of the workshop.

Topics of interest:

·   Representation learning

·   Multisensory learning and fusion

·   Representation alignment for Human-Robot Interaction

·   Structured priors

·   Model-based RL and world models

·   Hierarchical learning and planning

·   Skill composition and decomposition

·   Transfer learning