ICRA 2024 Workshop on Co-design in Robotics: Theory, Practice, and Challenges

2024 International Conference on Robotics and Automation

Full-Day Workshop

Yokohama, Japan

Room G303 (North)

Thanks to everyone for a wonderful workshop!

We will be working on 2 major follow-ups to this workshop: a summary report of the discussion, and an RAL special issue on co-design.

If you would like us to keep you in the loop on either of these items, please let us know at: https://forms.gle/n8yiWUZPmDuVhXHDA


Robots are complex systems that must combine and integrate multiple coupled subsystems. The field of co-design seeks to understand how to jointly optimize robot subcomponents in order to produce a desired behavior. For example, one might consider how to jointly design the coupled kinematics and control architecture of the robot, taking into account their mutual dependencies, or how to design the robot mechanics and manufacturing methods. Co-design can also mean a process that engages end-users, stakeholders, and experts in the creation and development of robotic systems.

Robot co-design can offer various advantages – such as enhancing performance, efficiency, adaptability, usability, and user satisfaction – and there has been a large recent growth in both co-design methodologies and application areas. This workshop aims to gather researchers working in the various axes of co-design to discuss current trends, open problems, and challenges that the field is poised to tackle over the next 5-10 years.

Topics of interest include:


9:00 Welcome Remarks and Ground Rules
9:20 Short Talk: Audrey Sedal (McGill University)
"Co-Optimizing Design Control for Soft Robots in Contact with the Environment "
9:40 Short Talk: Matei Ciocarlie &  Zhanpeng He (Columbia University)
"(Co-)Designing Robots with Reinforcement Learning"
10:00 Coffee Break
10:20 Vignettes (10 min)
10:40 Tech Spotlights (2 min)
10:50 Breakout Discussion:
What are the technically challenging design areas that co-design should tackle over the next 5-10 years?
11:40 Reports and General Group Discussion
12:20 Lunch
14:00 Short Talk: Daniele  Pucci (Instituto Italiano di Tecnologia)
"Towards a Shared-Embodied Intelligence of Humanoid Robots: A Computational Human-Aware Co-Design Approach"
14:20 Short Talk: Guoxin Fang (Chinese University of Hong Kong)
"Improving Proprioception of Soft Robotics through Sensor/Actuator Co-Design: From Numerical Simulation to Data-Driven Methods"
14:40 Vignettes (10 min)
15:00 Tech Spotlights (2 min)
15:30 Coffee Break
16:00 Breakout Discussion:
What are the new areas of research / methods that will allow us to tackle these challenges?
17:00 Reports and General Group Discussion
17:30 Adjourn

Invited Speakers

Audrey Sedal

McGill University

Matei Ciocarlie

Columbia University

Daniele Pucci

Istituto Italiano di Tecnologia

Guoxin Fang

Chinese University of Hong Kong

Vignettes (Abstracts will be shared with participants at the workshop)

Morning Session (9:40 - 10:00)

Afternoon Session (14:40 - 15:00)

Tech Spotlights (Abstracts will be shared with participants at the workshop)

Morning Session (10:30-10:40)

Afternoon Session (15:00 - 15:30)

Call for Participation (Deadline extended to April 10)

We invite participants to submit 1-2 page abstracts on visionary ideas or recent results in robot co-design. Accepted abstracts will be featured in spotlight presentations (5 min. vignettes) or posters.

Abstracts may be submitted at https://forms.gle/eW54Y1xgHD5USCNy9 until April 10, 2024 (AOE). Acceptance notifications will be sent out 1-2 weeks after this date.

Travel Support

A limited number of travel grants are available to support participants who will present an abstract and participate in the discussion for the entire workshop. If you would like to be considered for the travel grant, please indicate on the abstract submission form by March 31.

Workshop Organizers

This workshop is jointly organized by the TC on Mechanisms and Design and the TC on Model-Based Optimization for Robotics

TC on Mechanisms and Design

TC on Model-Based Optimization for Robotics

Supported by: