Monday, July 15, 2024 (Auditorium Aula-20, Aula Conference Center, TU Delft, Netherlands)

Workshop on Embodiment-Aware Robot Learning (EARL)

at Robotics: Science and Systems (RSS)


This workshop brings together researchers working on co-design of robot embodiment and control algorithms




Stanford & Columbia University

Vrije Universiteit Amsterdam

Stanford University

Boston Dynamics

Columbia University

CSIRO Australia

University of Vermont




EARL Workshop at RSS'24

TU Delft, Netherlands

The workshop on Embodiment-Aware Robot Learning (EARL) brings the optimization of robot embodiment into the foreground of robot learning. Artificial Intelligence (AI) in robotics has come to primarily mean intelligent decision making within a fixed embodiment. However, natural intelligence is broader, and it includes optimization of the organism’s body for its ecological niche. We, therefore, seek to emphasize the importance of co-design of the agent’s body and behavior within the field of robot learning and AI, both from a conceptual point of view and in practical terms.

We welcome participants from various sub-communities, working on both hardware and software aspects of robotics, such as bioinspired robotics, field robotics, grasping and manipulation, robot learning, control & dynamics, mechanisms & design, and robot modeling & simulation, among others.



Call for Papers

We invite extended abstracts (2-4 pages excluding references, in RSS paper format) to be presented as lightning talks and posters during the workshop. We welcome submissions that report new results, ongoing research, and broad vision papers. All submissions are double-blind and peer-reviewed. We invite participants from a variety of backgrounds: robotics, machine learning, evolutionary optimization, mechanical design, electrical engineering.

Submission Deadline (Extended): June 14

Acceptance Notification: June 24

Camera-ready Paper Submission Deadline: July 7

Best poster award:
Carmelo Sferrazza, Dun-Ming Huang, Fangchen Liu, Jongmin Lee, Pieter Abbeel
Body Transformer: Leveraging Robot Embodiment for Policy Learning

Best paper award:
Sergio Hernández-Gutiérrez, Ville Kyrki, Kevin Sebastian Luck
Following Ancestral Footsteps: Co-Designing Agent Morphology and Behaviour with Self-Imitation Learning



Opening Remarks

09:00 - 09:30

Environment and Embodiment-Aware Design Optimisation

David Howard, Commonwealth Scientific and Industrial Research Organisation


A Full Stack Approach to Dexterous Manipulation

Pulkit Agrawal, Massachusetts Institute of Technology

Morning Coffee Break 10:00 - 10:30


Co-Adaptation of Robot Design & Behavior: A Reinforcement Learning Perspective

Kevin Sebastian Luck, Vrije Universiteit Amsterdam

11:00 - 11:30

Enabling Cross-Embodiment Policy Learning

Jeannette Bohg, Stanford University

11:30 - 12:00

Hardware and Policy Co-design with Deep Reinforcement Learning for Robotic Manipulators

Matei Ciocarlie, Columbia University

12:00 - 12:30

Lightning Talks by Workshop Participants

Lunch Break 12:30 - 14:00


Self-Adaptive Robots

Shuran Song, Stanford University

14:30 - 15:00

Structural Priors for Robot Learning

Michael Lutter, Boston Dynamics

15:00 - 15:30

On the Fragile Co-Adaptation of Brain and Body in Evolved Soft Robots

Nick Cheney, University of Vermont

Afternoon Coffee Break 15:30 - 16:00

16:00 - 17:00

Poster Session

17:00 - 17:15

Closing Remarks & Award Ceremony


TU Darmstadt

German Research Center for AI (DFKI)

TU Darmstadt & DFKI