Special Issue on Humanoid Robots

Aim and Scope

Every day, humans solve complex tasks, such as working in disaster scenarios, performing complex assembly tasks, which are difficult to solve once put into a formal task description. The concept of a humanoid robot represents the desire to develop machines that can mimic human form, movement, and intelligence, and thus accomplish all these tasks with human-like performance. For this reason, humanoid robots are an ideal platform that allows testing new algorithms and forcing the development of new ideas. In recent years, following the announcement of the Tesla Bot and the Chinese government's white paper on humanoid robots, we have seen an explosion of new, dynamic and very capable humanoid robots, which offers new robotic applications and research challenges.

It becomes evident that designing humanoid-like robots specialized for specific tasks could yield remarkable results as well or even better, e.g. quadrupeds equipped with wheels that can also be operated like a humanoid robot. Machine learning algorithms such as Reinforcement Learning are particularly applicable here and have therefore recently become popular. Looking at breakthroughs in computer vision and large language models, a similar breakthrough in embodied AI is beginning to emerge.

This Special Issue will provide an overview of current mechanical designs of humanoid and humanoid-like systems, as well as the algorithmic side, with a focus on machine learning. Simulation as an important tool is therefore also in the focus of this issue, without losing sight of real systems. Beyond basic research, this issue also explores the applications that have emerged, such as in logistics, healthcare, disaster response, and manufacturing, and answers the question of how close research has come to real-world application.

Topics of interest include but are not limited to the following:

  • Humanoid robot designs
  • Humanoid-like robots, e.g. hybrid designs of biped and quadrupeds or wheeled systems
  • Control (whole-body, optimization-based, reinforcement learning, etc.)
  • Sim-to-real gap (multi-contact, system identification, transfer, etc.)
  • Safety (constraints, guarantees, stability margins, ISO guidelines, etc.)
  • Loco-manipulation
  • Perception and planning for legged systems
  • Legged mobile manipulation
  • Benchmarking
  • Real-world applications and success stories, e.g. in logistics, healthcare, disaster response, manufacturing, domestic robotics, agriculture, construction, maintenance, sports etc.

Important Dates

May 1, 2024 - Submission deadline

August 1, 1024 - First decision communicated to authors

September 15, 2024 - Submission of revised papers 

November 20, 2024 - Final decision

December 10, 2024 - Final manuscript upload

March 10, 2025 - Publication

Associate Editor

Oliver Urbann

Oliver Urbann

Fraunhofer IML

Dortmund, Germany

urbann@ieee.org

Guest Editors

Fei Chen

Fei Chen

The Chinese University of Hong Kong

Hong Kong, China

feichen@cuhk.edu.hk

Julian EBer

Julian Eßer

Fraunhofer IML

Dortmund, Germany

julian.esser@iml.fraunhofer.de

Robert Griffin

Robert Griffin

Florida Institute for Human & Machine Cognition

Pensacola, FL USA

rgriffin@ihmc.org

Kenji Hashimoto

Kenji Hashimoto

Waseda University

Fukuoka, Japan

kenji.hashimoto@waseda.jp

Fumio Kanehiro

Fumio Kanehiro

National Institute of Advanced Industrial Science and Technology (AIST)

Tsukuba, Japan

f-kanehiro@aist.go.jp

Paul Oh

Paul Oh

University of Nevada, Las Vegas

Las Vegas, NV, USA

paul.oh@unlv.edu

Olivier Stasse

Olivier Stasse

Laboratory for Analysis and Architecture of Systems (LAAS), CNRS

Toulouse, France

ostasse@laas.fr

Yuichi Tazaki

Yuichi Tazaki

Kobe University

Kobe, Japan

tazaki@mech.kobe-u.ac.jp

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