Workshop on effective Representations, Abstractions, and Priors for Robot Learning (RAP4Robots)

Monday, 29th May @ ICRA 2023 @ CAPITAL SUITE 8


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:

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

Accepted Papers 




Animesh Garg

University of Toronto/ NVIDIA, Canada

Chelsea Finn

Stanford University, USA

Edward Johns

Imperial College London, UK

Franziska Meier

Facebook AI (FAIR), USA

Markus Wulfmeier

Deepmind, UK

Benjamin Rosman

 University of the Witwatersrand, South Africa

George Konidaris

Brown University, USA



Georgia Chalvatzaki

TU Darmstadt, Germany

Jeannette Bohg

Stanford University, USA

Takayuki Osa

University of Tokyo, Japan

Oliver Kroemer

Carnegie Mellon University, USA

Fabio Ramos

University of Sydney/ NVIDIA, Australia

Snehal Jauhri

TU Darmstadt, Germany

Ali Younes

TU Darmstadt, Germany

Mohit Sharma

Carnegie Mellon University, USA

Extended program committee:


Supported by the IEEE RAS Technical Committees on: