×
This humanoid robot learned to waltz by mirroring human movements
Written by
Published on
Join our daily newsletter for breaking news, product launches and deals, research breakdowns, and other industry-leading AI coverage
Join Now

A new AI system called ExBody2 enables humanoid robots to mirror human movements with unprecedented fluidity, allowing them to perform complex actions like dancing, walking, and fighting moves.

Key innovation: Researchers at the University of California, San Diego have developed an AI system that helps robots learn and replicate human movements more naturally than traditional pre-programmed sequences.

  • The system uses motion capture recordings from hundreds of human volunteers to build a comprehensive database of movements
  • ExBody2 employs reinforcement learning to teach robots how to perform various actions through trial and error
  • The AI first learns using complete virtual robot data before transitioning to real-world sensor measurements

Technical approach: The system leverages the structural similarities between humanoid robots and humans to efficiently transfer human motion data into robotic movements.

  • Researchers created a database of basic and complex movements that robots could potentially perform
  • The AI system coordinates multiple body parts simultaneously for more natural movement
  • Training occurs in two phases: first with complete virtual data access, then with only real-world sensor data

Practical applications: ExBody2 has successfully demonstrated its capabilities on commercial humanoid robots.

  • Robots can now perform complex sequences including 40-second dance routines
  • The system enables smooth transitions between different movements like walking and crouching
  • Robots can execute interactive movements such as waltzing with human partners
  • Combat movements like throwing punches are also possible

Expert perspective: Lead researcher Xuanbin Peng explains the significance of full-body coordination in robotics.

  • “Humanoid robots work best when they coordinate all their limbs and joints together”
  • “Many tasks and motions require the arms, legs and torso to work together”
  • The approach could potentially allow robots to learn any movement that humans can perform

Future implications: While ExBody2 represents a significant advancement in robotic movement, questions remain about its real-world applications and limitations.

  • The technology could lead to more natural human-robot interactions in various settings
  • Further research may be needed to ensure safety and reliability in dynamic environments
  • The system’s ability to adapt to unexpected situations and maintain balance during complex movements requires additional testing
Humanoid robot learns to waltz by mirroring people's movements

Recent News

7 ways to optimize your business for ChatGPT recommendations

Companies must adapt their digital strategy with specific expertise, consistent information across platforms, and authoritative content to appear in AI-powered recommendation results.

Robin Williams’ daughter Zelda slams OpenAI’s Ghibli-style images amid artistic and ethical concerns

Robin Williams' daughter condemns OpenAI's AI-generated Ghibli-style images, highlighting both environmental costs and the contradiction with Miyazaki's well-documented opposition to artificial intelligence in creative work.

AI search tools provide wrong answers up to 60% of the time despite growing adoption

Independent testing reveals AI search tools frequently provide incorrect information, with error rates ranging from 37% to 94% across major platforms despite their growing popularity as Google alternatives.