Objectives

The field of robotic chefs in the kitchen presents unique challenges that require bimanual manipulation to attain human-level dexterity. The incorporation of a second robot arm is widely believed to enhance the manipulation capabilities and open up new research and practical applications. However, identifying the overarching challenges and specific module-related obstacles that have impeded progress in leveraging this novelty for the benefit of humans remains a pressing issue. Addressing these challenges necessitates a comprehensive, interdisciplinary perspective that goes beyond solving isolated component-level problems. It is imperative for roboticists from academia and industry to collaborate, jointly reviewing, re-investigating, reshaping, and driving advancements in this research domain (ICRA2024 theme “CONNECT+”). To guide our discussions, we have formulated the following question as a focal point for the workshop:

For the sake of real-world application:

• What are the key challenges in bimanual manipulation for robotic chefs in the kitchen?

• How can interdisciplinary collaboration enable innovative solutions, overcoming these obstacles to advance the research and practical implementation of robotic chefs?

•  How to transfer bimanual manipulation skills from human expert chefs to robots?

• How to benchmark the robot’s cooking abilities?

• What kind of sensor modalities are useful for robotic kitchen manipulation tasks?

• How to best exploit the roles of manipulators for different functions in kitchen manipulation tasks, sensing, grasping, manipulation, exploration, etc?

• How could computing tools, such as neural networks (including LLM), be a game changer for the dual/multi-arm coordination and manipulation for robotic kitchen tasks?

• How to model the cooperation between human and robot chiefs so that the task can be implemented in an optimal way by taking into consideration the human preferences?

Call for Contributions:

Topics of interest:

• Bio-inspired/human-inspired bimanual manipulation theories and studies

• Bimanual manipulation task coordination and planning

• Bimanual manipulation task sensing and learning

• Bimanual manipulation control for robot arms and hands

• Bimanual (loco-)manipulation with various mobile platforms, wheels, legged, wheel-leg hybrid, etc.

• System communication and optimization for effective bimanual manipulation

• Robot mechanics and electronics for bimanual manipulation setup

• Bimanual manipulation is applied in manufacturing, healthcare, logistics, maintenance & inspection, etc.

• Multi-arm manipulation beyond dual-arm bimanual manipulation

Researchers interested in presenting their work during the workshop can submit an extended abstract in the standard IEEE conference template, 1-4 pages, excluding references. Please note that it is possible to use material that has already been presented at a previous conference.  Abstracts will be reviewed by the organizers. We welcome submissions about novel results, work in progress, literature reviews, and perspectives/opinions. The authors of accepted papers will be invited to present their results in a poster session during the workshop. Authors of accepted abstracts will have to prepare a poster and present it in person during the workshop.  

The goal of the workshop is to provide a forum for interdisciplinary discussion and a unique opportunity for a fruitful and in-depth exchange of ideas between early-career and advanced-career researchers. The accepted posters/papers will be invited to a Special Issue in a journal   

• Extended abstracts should be submitted by e-mail to: tao.teng@cuhk.edu.hk; 

Important dates:

Submission Deadline: April 10, 2024 (23:59 PST)

Notification date: April 20, 2024 (23:59 PST)

Final submission: May 1, 2024 (23:59 PST)

Workshop date: May 13, 2024

Special Issues

Special Issue on Empowering Robots in the Kitchen: Learning Manipulation Skills

Introduction:

The realm of robotic chefs in the kitchen presents unique challenges that demand advanced manipulation skills to achieve human-level dexterity. Various approaches, including imitation learning and deep reinforcement learning, have been proposed to enable robots to acquire manipulation skills for complex tasks. While significant progress has been made in this area, there remains a considerable challenge in terms of the practical implementation of robotic chefs. Tackling these challenges requires a comprehensive, interdisciplinary perspective. Manipulation skills learning and generalization, i.e., the ability to reuse and adapt the learned skills, has gradually become a research focus in the domains of robotics and AI. In the kitchen, these skills encompass activities such as cutting ingredients, measuring quantities, stirring mixtures, and assembling recipes. By acquiring manipulation skills, robots can assist in time-consuming and repetitive tasks, making cooking more efficient and convenient.

This Special Issue presents the newest findings, approaches and in the field of robotic manipulation in kitchen scenarios. We aim to investigate what robots can exactly achieve so far in terms of autonomous manipulation for kitchen tasks, and how to go beyond state-of-the-art toward next-step higher autonomy. Submissions of related topics in academic research and industry are welcome.

Topics covered:

• Skill acquisition and transfer for robotic chefs

• Perception and sensing for robotic chefs

• Human-robot interaction in the kitchen

• Autonomous kitchen manipulation

• Safety control for robotic cooking

• Benchmarking robotic manipulation of culinary tasks

Important Deadlines:

• Submission deadline: 1 May 2024

• First decision to authors 30 June 2024

• Resubmission 15 August 2024

• October 2024 publication

Submission Guidelines

Submission link: https://www2.cloud.editorialmanager.com/ijairr/default2.aspx?pg=login.asp&username= 

Editors/Guest Editors:
Tao Teng, Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Italy.
Email: tao.teng@unicatt.it

Chao Zeng, Department Informatics, University of Hamburg, Germany
Email: chaozeng@ieee.org

Fei Chen, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong
Email: f.chen@ieee.org

Miao Li, Wuhan University, China.
Email: miao.li@whu.edu.cn

Workshop Organizers

• Tao Teng, Postdoctoral Fellow, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong | Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Italy. Email: tao.teng@cuhk.edu.hk (main contact)

• Fei Chen, Assistant Professor, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong SAR | Department of Advanced Robotics, Italian Institute of Technology, Italy.  Email: f.chen@ieee.org 

• Miao Li, Associate Professor, Wuhan University, China. Email: miao.li@whu.edu.cn

• Sylvain Calinon, Senior Researcher, Idiap Research Institute, Switzerland. Email: sylvain.calinon@idiap.ch

• Yi Guo, Professor, Stevens Institute of Technology. Email: yguo1@stevens.edu 

Support TC

We are grateful for the support of the following organizations:

• Neuro-robotics Systems 

• Collaborative Automation for Flexible Manufacturing