The Data Science Lab
since 2005
  • Home
  • Research
      • Research grants
      • Research interests
      • Research leadership
      • Student theses
      • Humanoid Ameca
      • AI Server
        • GPU
        • Request
        • Allocation
  • Consultancy
      • Consulting projects
      • Cooperate training
      • Enterprise innovation
      • Impact cases
      • Our clients
      • Partnership
  • People
      • Awards and honors
      • Staff
      • Team members
  • Activities
      • Events and services
      • Talks
      • Tutorials
      • Workshops
  • Publications
  • Communities
      • ACM ANZKDD Chapter
      • Big data summit
      • Data Analytics book series
      • DSAA conferences
      • IEEE TF-DSAA
      • IEEE TF-BESC
      • JDSA Springer
      • DataSciences.Info
      • MQ's AI Lab
  • Spotlights
      • Actionable knowledge discovery
      • Agent mining
      • AI: Artificial-intelligence
      • AI4Tech: AI enabling technologies
      • AI4Finance: AI for FinTech
      • AI robots & humanoid AI
      • Algorithmic trading
      • Banking analytics
      • Behavior analytics, computing, informatics
      • Coupling and interaction learning
      • COVID-19 global research and modeling
      • Data science knowledge map
      • Data science dictionary
      • Data science terms
      • Data science tools
      • Data science thinking
      • Domain driven data mining
      • Educational data mining
      • Large-scale statistical learning
      • Metasynthetic engineering
      • Market surveillance
      • Negative sequence analysis
      • Non-IID learning
      • Pattern relation analysis
      • Recommender systems
      • Smart beach analytics
      • Social security analytics
      • Tax analytics
  • About us
CSUR: Humanoid Robots and Humanoid AI: Review, Perspectives and Directions

Humanoid Robots and Humanoid AI: Review, Perspectives and Directions
Longbing Cao. ACM Computing Surveys, Volume 58, Issue 4, Article No.: 97, 1-37, 2025.

In the approximately century-long journey of robotics, humanoid robots made their debut around six decades ago. While current humanoids bear human-like appearances, none have embodied true humaneness, remaining distant from achieving human-like to human-level intelligence. The rapid recent advancements in generative AI and (multimodal) large language models have further reignited and escalated interest in humanoids towards real-time, interactive, and multimodal designs and applications, such as fostering humanoid workers, advisers, educators, medical professionals, caregivers, and receptionists. These unveil boundless opportunities of transforming 1) AI robotics into a research era of humanoid AI, and 2) AI robots into new-generation humanoid AI robots (AI humanoids). Our unique and comprehensive review of about 30 reported humanoids discloses a systematic terminology and a paradigmatic landscape of human-looking to human-like and human-level humanoids. It inspires comprehensive new perspectives and directions of humanoid AI as an area: transitioning from human-looking to humane humanoids, humanizing humanoids with functional and nonfunctional specifications, and cultivating technical and actionable advances of AI humanoids. Humanoid AI and AI humanoids nurture symbiotic advancements and future opportunities of synthesizing and transforming humanity modeling and conventional, generative to human-level AI into humanoid robotics.

Access the paper at CSUR https://dl.acm.org/doi/10.1145/3770574.

About us
School of Computing, Faculty of Science and Engineering, Macquarie University, Australia
Macquarie University Frontier AI Research Centre
Level 3, 3 Innovation Road, Macquarie University, NSW 2109, Australia
Tel: +61-2-9850 9583
Staff: firstname.surname(a)mq.edu.au
Students: firstname.surname(a)student.mq.edu.au
Contacts@datasciences.org