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ROBOTICS / Machines that move

FROM INDUSTRIAL ARMS → HUMANOIDS

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FROM INDUSTRIAL ARMS → HUMANOIDS Key sections include: ROBOTICS / Machines that move; The word.; Three Laws.; Unimate at GM.; The industrial era.; Sense. Plan. Act.; SLAM : build a map of a place you've never been.; Boston Dynamics.; Manipulation is still hard.; The humanoid wave..

Key sections

  • 01ROBOTICS / Machines that move
  • 02The word.
  • 03Three Laws.
  • 04Unimate at GM.
  • 05The industrial era.
  • 06Sense. Plan. Act.
  • 07SLAM : build a map of a place you've never been.
  • 08Boston Dynamics.
  • 09Manipulation is still hard.
  • 10The humanoid wave.
  • 11The AI brain.
  • 12Where the money goes.
  • 13References & further viewing.

Topics covered

catalogtechrobotics

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Slide outline
  1. 01ROBOTICS / Machines that move
  2. 02The word.
  3. 03Three Laws.
  4. 04Unimate at GM.
  5. 05The industrial era.
  6. 06Sense. Plan. Act.
  7. 07SLAM : build a map of a place you've never been.
  8. 08Boston Dynamics.
  9. 09Manipulation is still hard.
  10. 10The humanoid wave.
  11. 11The AI brain.
  12. 12Where the money goes.
  13. 13References & further viewing.
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Presentation Transcript

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Slide 01

ROBOTICS / Machines that move

  • // DECK 13 / TECH-ROBOTICS / 2026
  • FROM INDUSTRIAL ARMS → HUMANOIDS
  • A short tour of the field: from R.U.R. on a 1920s stage to bipeds
  • walking onto factory floors a century later.
  • SAFETY: KEEP CLEAR / WATCH HANDS
Slide 02

The word.

  • 01 / ETYMOLOGY
  • The term robot enters language in 1920,
  • in Karel Čapek's play
  • R.U.R. — Rossum's Universal Robots. Coined by his brother Josef,
  • it derives from Czech robota: forced labor, drudgery.
  • The play's robots are not metal. They are mass-produced synthetic workers
  • who eventually rise against their makers — the template for a century of
  • anxiety about machine labor.
  • > PLAY : R.U.R.
  • > AUTHOR : KAREL ČAPEK
  • > PREMIERE : PRAGUE, 25 JAN 1921
  • > ETYMOLOGY : robota (Cz.) — forced labor
Slide 03

Three Laws.

  • 02 / FICTION SHAPES POLICY
  • Isaac Asimov, Runaround (1942). A fictional safety framework
  • embedded in every positronic brain — and a permanent reference point
  • for real-world AI ethics debates.
  • Law 01
  • A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  • Law 02
  • A robot must obey orders given it by human beings, except where such orders conflict with the First Law.
  • Law 03
  • A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
  • Asimov spent decades writing stories about how cleverly these laws fail.
  • Real robots run on ROS, not ethics — but the framing stuck.
Slide 04

Unimate at GM.

  • 03 / 1961
  • The first industrial robot enters service at General Motors' Inland Fisher
  • Guide plant in Ewing Township, NJ. It was a 4,000-lb hydraulic arm built by
  • George Devol and Joseph Engelberger, lifting hot die-cast parts that would
  • have maimed humans.
  • > UNIT : UNIMATE 1900
  • > YEAR : 1961
  • > SITE : GM TRENTON / DIE CASTING
  • > PAYLOAD : ~225 kg
  • > CONTROL : MAGNETIC DRUM, HYDRAULIC
Slide 05

The industrial era.

  • 04 / SCALING THE ARM
  • By the 1980s, Japan, Germany, and Switzerland turned the industrial arm
  • into a global commodity. Six revolute joints, repeatable to fractions of
  • a millimeter, bolted to factory floors worldwide.
  • FANUC
  • Yamanashi, Japan. Yellow arms. ~750k installed.
  • ABB
  • Zürich. White arms. IRB lineage since 1974.
  • KUKA
  • Augsburg, Germany. Orange arms. KR series.
  • > AXES : 6 (BASE/SHOULDER/ELBOW/WRIST×3)
  • > REPEAT : ±0.02 mm
  • > FLEET : ~3.5 M operational worldwide (2024)
Slide 06

Sense. Plan. Act.

  • 05 / CANONICAL LOOP
  • Every robot, from a Roomba to Atlas, runs some version of this loop.
  • The 1980s "subsumption architecture" debate was about whether you could
  • skip the plan stage; modern systems blend reactive and deliberative layers.
  • SENSE — cameras, LIDAR, IMU, force/torque, joint encoders.
  • PLAN — state estimation, motion planning, task graphs.
  • ACT — joint torques, end-effector commands, locomotion gaits.
  • Loop frequency matters: 1 kHz at the joint, 30–200 Hz at perception,
  • ~10 Hz at task. Latency is the hidden enemy.
Slide 07

SLAM: build a map of a place you've never been.

  • 06 / NAVIGATION
  • Simultaneous Localization and Mapping. The chicken-and-egg problem at the
  • heart of mobile robotics: to know where you are, you need a map; to make
  • a map, you need to know where you are. SLAM solves both at once,
  • probabilistically.
  • EKF-SLAM (1990s) — extended Kalman filter, gaussian beliefs, doesn't scale.
  • FastSLAM / GraphSLAM (2000s) — particle filters, pose graphs, loop closure.
  • Visual SLAM — ORB-SLAM, DSO; cameras only, runs on a phone.
  • NeRF / 3D Gaussian SLAM (2023+) — dense photoreal reconstruction in the loop.
  • > KEY OPS : feature extract → data assoc → optimize pose graph → close loop
  • > FAILURES : kidnapped robot, perceptual aliasing, dynamic scenes
Slide 08

Boston Dynamics.

  • 07 / DYNAMIC LOCOMOTION
  • Spun out of MIT's Leg Lab in 1992 under Marc Raibert. They proved a robot
  • could fall and not fall — that controlled instability beat statically
  • stable plodding. The viral videos pulled the field forward by years.
  • BigDog · 2005
  • DARPA-funded gas-powered quadruped. Shoved on ice, recovers. The "do not anger it" video.
  • Atlas · 2013→
  • Hydraulic, then electric (2024). Backflips, parkour, picking parts on a mock factory floor.
  • Spot · 2019
  • First commercial product. Inspections at oil rigs, construction sites, hospitals. ~$75k.
  • → search: boston dynamics atlas (YouTube)
Slide 09

Manipulation is still hard.

  • 08 / THE HARD PART
  • ⚠ Unsolved
  • Walking and driving are largely solved. Picking up a strawberry without
  • crushing it, opening a ziplock bag, threading a cable — these remain
  • open problems. The reasons are physical, not algorithmic.
  • Contact dynamics — friction, deformation, slip. Hybrid systems are hard to plan through.
  • Tactile sensing — GelSight, BioTac give rich signals; few hands have them at scale.
  • Dexterity — the human hand has 27 DoF. Most robot hands have 4–16, with weak fingertips.
  • Dataset gap — internet has billions of images but few demonstrations of contact.
  • > HUMAN HAND : 27 DoF, ~17,000 mechanoreceptors
  • > SHADOW HAND: 24 DoF, ~$100k+
  • > GAP : not joint count — sensing + control bandwidth
Slide 10

The humanoid wave.

  • 09 / 2022 — ?
  • A surge of well-funded entrants betting that human-shaped robots can
  • drop into existing human environments — warehouses, homes, factories —
  • without retooling.
  • Tesla Optimus — vertically integrated, leveraging FSD stack.
  • Figure — partnered with BMW, OpenAI ties, Bay Area.
  • 1X (Norway) — NEO Beta soft-shell home humanoid.
  • Apptronik — Apollo, Mercedes pilots.
  • Unitree (China) — G1, H1; aggressive pricing under $20k.
  • As of 2025: pilots in warehouses. Unit economics, MTBF, and safety
  • certification are the gating questions, not motion.
Slide 11

The AI brain.

  • 10 / WHAT CHANGED
  • 2022 → 2025
  • For 50 years, robotics control was hand-engineered. The recent shift:
  • large pretrained models — vision, language, and action — fine-tuned on
  • robot data, generalize across tasks the way LLMs generalize across text.
  • RT-1 / RT-2 (Google, 2022–23) — transformer policies on real robots.
  • OpenVLA (2024) — open-source vision-language-action, 7B params, fine-tunable.
  • π0 / π0.5 (Physical Intelligence, 2024–25) — flow-matching action heads.
  • Gemini Robotics (2025) — multimodal foundation models targeting embodiment.
  • > INPUT : RGB frames + language instruction
  • > OUTPUT : end-effector pose / joint deltas @ 5–50 Hz
  • > UNLOCK : zero-shot tasks, language-conditioned policies, sim-to-real transfer
Slide 12

Where the money goes.

  • 11 / DEPLOYMENT
  • WAREHOUSES
  • Amazon, Symbotic, Locus. Picking, sorting, end-of-line. Highest-volume near-term market.
  • ELDERCARE
  • Japan + Europe demographics. Lift assist, fall detection, social companions. Slow regulatory path.
  • SURGERY
  • Intuitive's da Vinci has done 14M+ procedures. Tele-operated, not autonomous — yet.
  • AGRICULTURE
  • Tractors steer themselves; weeders, pickers, dairy bots fill labor gaps.
  • LOGISTICS
  • Autonomous trucks, last-mile delivery rovers, sidewalk bots. Regulation-bound.
  • DEFENSE
  • Drones, ground vehicles, perimeter security. The fastest-deploying segment, and the one with the most ethical weight.
Slide 13

References & further viewing.

  • 12 / CLOSING
  • A starter set. None are exhaustive; all are useful.
  • Books — Siegwart & Nourbakhsh, Intro to Autonomous Mobile Robots; Murray, Li, Sastry, A Mathematical Introduction to Robotic Manipulation.
  • Open source — ROS / ROS2, MoveIt, Drake, Isaac Sim, MuJoCo.
  • Courses — Underactuated Robotics (Russ Tedrake, MIT); Modern Robotics (Kevin Lynch, Northwestern).
  • Reports — IFR World Robotics annual; Robot Report.
  • // VIDEO SEARCHES
  • youtube → boston dynamics atlas
  • youtube → industrial robotics unimate
  • END OF DECK / 13 SLIDES / SAFETY: KEEP CLEAR
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