OpenAI Investment in 1X Technologies

• OpenAI led $23M investment in Norwegian humanoid robotics company 1X Technologies
• Company formerly called Halodi, unrelated to Wyoming cable company with same name
• Investment aligns with OpenAI's early product goal: household robot capable of setting/clearing tables
• Goal somewhat forgotten due to ChatGPT focus

Humanoid Robot Definition and Requirements

• No widely accepted definition exists
• Key characteristics from design purpose:
  • Move around and perform broad spectrum of tasks
  • Physically touch people without harm
  • Manipulate human-designed tools
  • Autonomously operate in human environments
  • Communicate at advanced level
• Design requirements dictate human-like form:
  • Torso, head, two legs, two arms
  • Hands with multiple fingers
  • Two legs preferred over four for energy, humanity, cost reasons

Industrial vs Humanoid Robots

• Industrial robots: nailed to ground, specific tasks, big/heavy/fast
• Not meant for regular human interaction
• Humanoid robots: operate in tight intimate spaces with humans
• Requires special engineering considerations

Historical Development

Early Theoretical Foundation (1968)

• Zero Moment Point (ZMP) stability concept introduced by Miomar Vukobratovich
• Covers dynamics and control of legged robots with powered controllable joints
• Critical for balance: robot supports itself on single foot 80% of walking time
• ZMP represents ground point where robot's footing can be stable

Waseda University Research (1973-1984)

• Dr. Ichiro Kato's team started humanoid robot research based on prosthesis work
• 1973: WABOT-1 debuted - walked on two legs, held things, communicated in Japanese
  • Mental faculties judged equivalent to 1.5-year-old
• 1984: WL-10RD - first dynamically balanced bipedal robot
  • First physical demonstration of ZMP concept feasibility
• WABOT-2: could sight-read music, play electric organ
  • Guided by 80 microprocessors, mechanically produced using wires

Honda's Breakthrough (1986-1996)

• 1986: Honda started humanoid robot research program
• Goal: sophisticated robot as work partner/servant
• First robot E0 (electronics): walked very slowly (5-30 seconds between steps), straight line only
• 1996: P2 introduced - fully self-contained, walked on flat surfaces and stairs
• P2 breakthrough: proved bipedal humanoid robots were possible (many were skeptical)
• Evolved into government-sponsored humanoid robotics project for domestic helper robots

Current Technical Limitations

Walking Challenges

• ZMP-based robots have recognizable characteristics:
  • Heavy flat-footed walk
  • One foot always in full ground contact when other is airborne
• Human body: 300 degrees of freedom, uses up to 20 while walking
• Robots: simplified with frozen/rigidly controlled joints → unnatural movement
• Common result: bent knee, frozen torso walking
  • Keeps core stable, easier to calculate
  • Extremely tiring for robot and human
• Lack of "toe off" behavior (trailing big toe pushes off ground)
  • Important energy-saving feature missing in most robots

Obstacle Navigation

• Previous walking robots couldn't handle unexpected obstacles >6% of leg length
• Shorter than average curb height
• Issues calculating new coordinates for unexpected obstacles on-the-fly
• Recent progress using AI for approximate results without full model calculation

Recent Advances (2018)

• French team: robot climbing normal different-sized steps without tethers
• American team/Boston Dynamics: taught Atlas straight-leg walking
• Straight-leg walking and toe-off behavior emerged naturally after biasing controller
• Bird-inspired robots (e.g., Cassie by Agility Robotics):
  • Run 100 meters in under 25 seconds
  • Better navigate unstable terrain
  • Act/run like birds (dinosaurs) - affects human interaction

Manipulation Challenges

Moravec's Paradox

• Things humans find instinctively easy (clearing table) are very difficult for robots
• Example: picking up dirty plate requires:
  • Optimal trajectory approach
  • Fingers slide along plate edge, roll under
  • Push hand/fingers forward for grip
  • Manipulate, tip up, lift
• Requires exceptional mental calibration and soft touch

Soft Actuation Technology

• Being gentle requires: precise feedback, acute physical awareness, controlled stiffness
• Machines good at: strong, rigid, powerful
• Not good at: soft, huggable
• Soft actuation remains developing field requiring expertise in:
  • Materials, structural design, fabrication
• Computationally intense: soft material (often silicone) has infinite dimensions
• Researchers assume rigid rather than soft body conditions → errors

Sensor Limitations

• Need lots of sensing and feedback
• Sensors must be cheap and easily manufactured
• Experiments used: embedded liquid metal, rare earth magnets, optical fiber
• Challenge: balancing price and sensitivity

Power and Manufacturing Issues

• Most common soft actuators use pneumatics
• Require power-hungry, loud, high-vibration support systems (pumps)
• Industrial arms can pick up plates well (gentleness aside)
• Humanoid robots need to perform complete sequences under uncertain conditions

AI and Robotics Integration

Adaptive Skill Coordination (Facebook Research)

• Chains together lower-level individual skills
• Navigate toward bigger long-term goals
• Allows robot to back up and correct when things go wrong
• Trained four-legged Boston Dynamics robot for pick-and-place tasks between apartments

Simulation Training

• Video game technologies produce simulated worlds for training
• Huge boon for field - create simulated worlds without actual robots
• RoboCup 3D Soccer Simulation: uses Nao humanoid robot as agent
• Generates data for low-level behaviors: walking, standing, turning, kicking

Large Language Models (LLMs)

• Application not entirely obvious - LLMs trained on text, no physical bodies
• Not aware of physical context → strange answers (suggesting vacuum for spilled Coke when none nearby)
• Need to provide context through prompt engineering
• Google research: trained language model with low-level skills (body + brain)
• Microsoft suggestion: use LLM to translate ordinary communications to machine-understandable code/API
• ChatGPT still provides incorrect answers - human must remain in loop

Economic and Technical Challenges

Complexity Comparison

• Humanoid robots more challenging than self-driving cars:
  • Way more degrees of freedom
  • More intimate human interaction
  • Unknown physical form (vs known car form)
• Economics complicated - price and manufacturability critical but barely touched
• Critical points that matter significantly

Investment Interest

• Funding events for companies: Aptronic, Agility, 1X, Sanctuary
• Some machines starting to enter warehouses
• New AI methods expected to accelerate progress quickly