Galatea is proposed as a new platform capability domain for the Oshun monorepo, providing a comprehensive, full-stack humanoid robotic mannequin and fashion robotics platform.
Proposed Domain Name: Galatea (Greek — the statue brought to life by
Pygmalion's love and Aphrodite's blessing) Scope: scope:galatea Type:
Platform Capability Domain Date: February 2026
Galatea is proposed as a new platform capability domain for the Oshun
monorepo, providing a comprehensive, full-stack humanoid robotic mannequin and
fashion robotics platform. Like Oya (drone swarm intelligence), Aja (motion
intelligence), and Bellona (engine bridges), Galatea is a capability domain
consumed by product domains rather than being a standalone product itself.
The domain encompasses the complete vertical stack — from embedded firmware
running on motor controllers to cloud-based fleet management dashboards —
purpose built for deploying humanoid robots as fashion mannequins in retail
environments. These robots replace static mannequins with dynamic, intelligent
machines capable of walking runways, holding editorial poses, changing outfits,
responding to customers, and performing synchronized in-store fashion shows —
indefinitely, without fatigue.
No integrated platform like this exists in the industry today. Individual
subsystems exist in isolation (Boston Dynamics for locomotion, Engineered Arts
for realistic faces, Agility Robotics for warehouse humanoids), but no platform
unifies them around the fashion retail use case. Galatea is a blue-ocean
platform.
The domain encompasses:
Embedded firmware and real-time motor control (Rust/C on RTOS)
Humanoid kinematics, gait planning, and balance control
Morphable chassis with actuated body proportion adjustment
Fashion-native pose libraries and runway choreography
Multi-robot synchronized show engine
Computer vision, SLAM, and audience awareness
Generative AI for natural human-like movement
Garment fitting, tracking, and wardrobe management
Fleet management with predictive maintenance
Retail analytics and engagement measurement
Physics simulation and digital twin
Safety certification and regulatory compliance (ISO 13482, ISO/TS 15066)
Estimated Library Count: 148 libraries across 20 architectural modules.
In Greek mythology, the sculptor Pygmalion carved a woman from ivory with such
extraordinary artistry that the statue surpassed every living woman in beauty.
He fell in love with his own creation, and the goddess Aphrodite — moved by his
devotion — breathed life into the statue. The ivory warmed under his touch. She
opened her eyes. She was named Galatea ("she who is milk-white," from
gala, milk — evoking the luminous ivory from which she was carved).
Galatea is the archetypal mannequin brought to life.
Why Galatea fits:
The literal metaphor — She IS a mannequin that came to life. No other
mythological figure maps more precisely to the concept of a humanoid fashion
robot. A statue, crafted to be beautiful, given the ability to move, feel, and
exist in the world alongside humans.
Fashion and beauty origin — Pygmalion sculpted her to be the embodiment of
ideal beauty. The entire mythology centers on aesthetic perfection — precisely
what fashion mannequins represent.
Aphrodite connection — Aphrodite (already an Oshun domain for live
streaming) brought Galatea to life, creating a natural mythological link
between domains. Galatea exists because Aphrodite willed it.
Transformation narrative — The transformation from inanimate object to
living being mirrors the technological transformation of static mannequins
into intelligent, moving robots.
Female deity convention — Maintains the Oshun naming pattern of goddess
and feminine mythological figures.
Greek mythology diversity — Adds Greek mythology to the existing mix of
Yoruba (Oshun, Yemaya, Aja, Aje, Oya), Egyptian (Isis, Hathor), Hindu (Shakti,
Tara, Saraswati, Lakshmi), Buddhist (Kuan Yin), Celtic (Brigid, Airmid), Roman
(Bellona, Veritas), Norse (Freya), Shinto (Uzume), and Akan (Asase).
Cultural resonance — The Pygmalion/Galatea myth has profoundly influenced
Western art, literature, theater (Pygmalion by George Bernard Shaw, My Fair
Lady), and technology (the "Pygmalion effect" in AI). It immediately
communicates the concept to stakeholders.
Architecture Note: The capabilities below form the classical control
fallback layer. In normal operation, end-to-end neural policies (Section
2.9) handle whole-body control. These classical algorithms serve as: (a)
verified safety fallback when policy confidence drops below threshold, (b)
reference implementation for validation and certification, and (c) simulation
ground truth for training data generation.
Software Architecture: System 1 / System 2 (Industry Standard)#
Galatea adopts the System 1 / System 2 dual-process architecture that has
become the industry standard across all leading humanoid robotics firms (Boston
Dynamics Atlas, Figure AI Helix, NVIDIA GR00T, Agility Robotics Digit):
text
┌─────────────────────────────────────────────────────────────────┐
│ SYSTEM 2 — DELIBERATIVE REASONING (7–30 Hz) │
│ ├── Language understanding (LLM / VLM) │
│ ├── Task planning & decomposition │
│ ├── Scene understanding & semantic reasoning │
│ ├── Fashion show choreography interpretation │
│ ├── Customer engagement strategy │
│ └── Outputs: subgoals, language-conditioned action primitives │
├─────────────────────────────────────────────────────────────────┤
│ SYSTEM 1 — FAST REFLEXIVE CONTROL (30–200 Hz, layered) │
│ ├── Layer A: Full DiT/LBM policy at 30 Hz │
│ │ ├── 450M+ param Diffusion Transformer (flow-matching) │
│ │ ├── Vision + proprioception → action chunks │
│ │ └── Outputs 4–8 step action sequences per inference │
│ ├── Layer B: Lightweight VLA reflex policy at 200 Hz │
│ │ ├── Distilled policy (<50M params) for fast reactions │
│ │ ├── Interpolates between DiT action chunks │
│ │ └── Handles reactive balance, obstacle avoidance │
│ ├── Contact-responsive compliant behavior │
│ └── Classical control fallback (MPC/PID) for verified safety │
├─────────────────────────────────────────────────────────────────┤
│ MOTOR CORTEX — LOW-LEVEL CONTROL (1 kHz on RT MCU) │
│ ├── <1M param LSTM / lightweight policy (cf. Agility Digit) │
│ ├── FOC current control, torque limiting │
│ ├── Joint-level safety enforcement │
│ └── Runs on STM32H7 MCU, independent of edge GPU │
└─────────────────────────────────────────────────────────────────┘
Paradigm Note: The humanoid robotics industry has undergone a fundamental
shift from classical control (PID, MPC, ZMP) to end-to-end learned control
policies. Tesla replaced ~300,000 lines of hand-coded control logic with a
single multitask neural network managed by ~2–3K lines of infrastructure code.
Boston Dynamics' Electric Atlas uses a 450M-parameter Diffusion Transformer
with flow-matching objective. Figure AI's Helix VLA runs at 200Hz with a
single set of neural network weights controlling all 35 DOF. Galatea follows
this paradigm: learned policies are the primary control mechanism; classical
control (MPC, impedance control) serves as the verified safety fallback.
Every leading humanoid robotics firm relies on large-scale teleoperation data to
train end-to-end neural policies. Galatea includes a first-class teleoperation
and data collection pipeline — this is not optional infrastructure but a
core capability required to achieve competitive AI performance.
Capability
Technology
Description
VR Teleoperation
Meta Quest 3 / Apple Vision Pro + TWIST2 whole-body retargeting
Operator wears VR headset with hand tracking; whole-body motion retargeted to Galatea skeleton in real-time
Sensor Suit Capture
Xsens MVN Awinda + force-sensing gloves
Full-body IMU suit + finger-level force sensing for high-fidelity teleoperation data (cf. Tesla teleoperation suits)
First-Person Video Learning
Head-mounted stereo cameras (operator POV)
Capture operator's visual perspective during demonstrations for video-conditioned policy learning (cf. Tesla approach)
Multi-Operator Parallel Capture
4–8 robot stations with simultaneous operators
Scale data collection linearly; target 500+ hours teleoperation data (cf. Figure AI 500h, BD/TRI 468h)
Auto-Labeling Pipeline
Foundation model annotation (Cosmos, SAM2, DINO)
Automatic segmentation, contact labeling, action annotation on collected episodes
Synthetic Data Augmentation
Isaac Sim domain randomization + Cosmos world generation
Multiply real episodes 10–100× with varied lighting, textures, physics; proven for sim-to-real transfer
This section details the industry-standard neural control approach that
Galatea adopts, matching the architectures at Tesla, Boston Dynamics, Figure
AI, and Agility Robotics.
Feature
Technology
Specification
Diffusion Transformer Policy
450M+ param DiT with flow-matching objective
Camera images + proprioception → 4–8 step action chunks at 30Hz on Jetson Thor (cf. BD Atlas 450M DiT at 30Hz)
VLA Reflex Policy
Distilled <50M param VLA from DiT
Lightweight reflex layer at 200Hz; interpolates DiT action chunks for fast reactive control (cf. Figure Helix 200Hz)
Single Multitask Network
One set of weights for all behaviors
Walking, posing, recovering, gesturing, dressing — single NN, no mode switching (cf. Tesla Optimus approach)
System 1/System 2 Split
System 1: DiT 30Hz + reflex 200Hz; System 2: 7–30Hz
Galatea implements a three-layer safety architecture:
text
┌─────────────────────────────────────────────────────────┐
│ LAYER 3: BEHAVIORAL SAFETY (Software — Edge Compute) │
│ ├── Social navigation (maintain distance from humans) │
│ ├── Customer proximity speed limiting │
│ ├── Behavioral state machine with safe states │
│ ├── Anomaly detection and graceful degradation │
│ └── Remote e-stop via fleet management │
├─────────────────────────────────────────────────────────┤
│ LAYER 2: CONTROL SAFETY (Firmware — Real-Time MCU) │
│ ├── Joint velocity and acceleration limits │
│ ├── Cartesian workspace boundary enforcement │
│ ├── Force/torque limiting at each joint │
│ ├── Self-collision avoidance │
│ ├── Dynamic stability monitoring (tip-over prevention) │
│ └── Graceful degradation on sensor failure │
├─────────────────────────────────────────────────────────┤
│ LAYER 1: HARDWARE SAFETY (Electronics — Fail-Safe) │
│ ├── Hardware current limiters per motor driver │
│ ├── Redundant position encoders (absolute + incremental)│
│ ├── Hardware watchdog timers (independent of software) │
│ ├── Category 0 e-stop (immediate power cut to motors) │
│ ├── Category 1 e-stop (controlled deceleration) │
│ ├── Safe Torque Off (STO) per IEC 61800-5-2 │
│ └── Mechanical joint hard-stops │
└─────────────────────────────────────────────────────────┘
8.2 ISO 13482 Compliance (Personal Care / Service Robot Safety)#
Note: ISO 13482:2014 is currently being revised. ISO/FDIS 13482 is in the
final approval phase and will broaden scope from "personal care robots" to
"service robots" in both personal and professional/commercial applications.
Galatea's compliance framework targets both the existing 2014 standard and the
forthcoming revision to ensure forward compatibility.
Requirement
Galatea Implementation
Hazard identification
Systematic HAZOP for all robot states (standing, walking, posing, show, charging, fault)
Risk assessment
Quantified risk per ISO 12100 with severity × probability × exposure
Speed limitation
Hardware-enforced max TCP velocity: 250 mm/s near humans (configurable)
Force limitation
ISO/TS 15066 Table A.2 body-region-specific limits for transient and quasi-static contact
Stability
Tip-over moment monitoring; if CoM projection exits reduced support polygon → controlled sit-down
Entrapment prevention
All joint gaps > 25 mm or < 4 mm (no finger entrapment per ISO 13854)
Electromagnetic safety
CE/FCC compliant shielding; no interference with pacemakers at > 30 cm
Operator training
Mandatory training system with certification tracking in fleet management
8.3 Force and Pressure Limits (ISO/TS 15066 Adapted)#
Applicability Note: ISO/TS 15066 is a Technical Specification for
collaborative industrial robots (cobots), not service/personal-care robots.
Its biomechanical force/pressure limits are the best available quantitative
reference for human-robot contact safety. Galatea adapts these limits as
engineering design targets, not as a direct certification claim. The primary
certification pathway is ISO 13482 (personal care robots), supplemented by a
bespoke risk assessment per ISO 12100. The ISO/TS 15066 values below serve as
conservative design limits that exceed the likely ISO 13482 requirements.
Body Region
Max Transient Force (N)
Max Quasi-Static Force (N)
Max Pressure (N/cm²)
Skull / Forehead
130
65
30
Face
65
45
20
Neck (sides/front)
150
75
25
Chest
140
70
25
Abdomen
110
55
20
Hand (back/palm)
200
100
30
Forearm / Upper arm
150
75
25
Upper leg / Knee
220
110
30
Lower leg
130
65
25
All Galatea robots enforce these limits in Layer 2 (firmware) via real-time
torque sensor monitoring and instantaneous motor current cutoff if any limit is
approached to within 80% threshold.
Test firmware + control software against simulated actuators and sensors before deploying to physical robot; catches regressions pre-integration
End-of-Line (EOL) Test Rig
Automated functional test station: exercises every joint, reads every sensor, validates communication buses, runs safety function checks. Pass/fail per unit.
Burn-in Station
48-hour continuous operation under load to catch infant-mortality failures
Force Calibration Rig
Precision load cells + anvils for calibrating per-joint force/torque limits to ISO/TS 15066 values
Anechoic Chamber
Noise measurement per operating mode (standing, walking, morphing) to validate < 40 dB
Each safety function has a documented Fault Mode, Effects, and Diagnostic
Analysis (FMEDA) per IEC 61508, and a Fault Tree Analysis (FTA) for
top-level hazards. These are required for SIL-2 certification and maintained as
living documents throughout the development lifecycle.
Key principle: The classical control layer operates as a safety
envelope that is always active and cannot be disabled by the neural policy.
The neural policy proposes actions; the classical envelope clips, limits, and
validates them before execution. This is essential for ISO 13482
certification: the safety case is built on the verified classical layer, not
on the learned policy.
All person detection and tracking runs on-device (Jetson Thor). No raw images leave the robot. Only anonymized aggregate metrics (dwell time, count, heatmap) are sent to cloud.
Biometric data
No biometric identification (face recognition, fingerprinting) is performed. Gaze estimation uses anonymized head-pose vectors, not identity-linked data.
GDPR compliance
Data minimization by design: collect only what is needed for engagement analytics. Right to erasure supported via fleet management console. Data retention defaults to 30 days for event logs, 90 days for aggregate analytics.
CCPA compliance
No sale of personal information. Opt-out mechanism via in-store signage and QR code.
Employee data
Teleoperation recordings stored in access-controlled datasets with operator consent. Used only for model training. Anonymized before sharing outside training team.
Retail partner data
POS integration data is tenant-isolated. Analytics are per-customer (retailer), never cross-tenant.
Audit trail
Immutable log of all data access, processing, and deletion events. Available to DPO on request.
56 DOF, 360° joints, 220 Nm/kg actuators, 4h battery; 450M param Diffusion Transformer (flow-matching) "Large Behavior Model" trained on 1,700+ hours; System 1/System 2 architecture; deployed at Hyundai and Google DeepMind (CES 2026)
No fashion aesthetics, no retail integration, no body morphing; Galatea's LBM architecture targets alignment with BD's approach
Engineered Arts (Ameca Gen 3)
Most realistic humanoid face/upper body
27 DOF face alone, automated viseme lip-sync, 29 units deployed worldwide (ICRA 2025); $100K-$500K
No bipedal locomotion, no body morphing, no fleet management, no fashion choreography
Figure AI (Figure 03)
General-purpose humanoid (Oct 2025)
1.7m/70kg, Helix VLA (System 1 at 200Hz, System 2 at 7–9Hz), 35 DOF, two onboard GPUs, 6-camera vision, tactile fingertips (3g sensitivity), 500h teleoperation training data, 5h runtime, 2kW wireless foot charging
No fashion specialization, no body morphing, no retail analytics, no show engine; Galatea targets architectural alignment with Figure's Helix dual-system pattern
Agility Robotics (Digit)
Bipedal warehouse logistics robot
3-layer AI: Motor Cortex (<1M param LSTM at 1kHz), Planning AI (MPC), Semantic AI (LLM); zero-shot sim-to-real; deployed with Amazon
Industrial aesthetic, no fashion persona, no show capability; Galatea targets alignment with Agility's 3-layer software pattern
Unitree (H1/G1/R1)
Affordable bipedal platforms
R1 at $5,900 (Jul 2025), 25kg; H1 with 864 Wh, <4h static
No fashion shell, no choreography, no garment management, limited runtime
Tesla (Optimus Gen 3)
General-purpose humanoid for manufacturing
Mass production Jan 2026 (Fremont); single custom SoC "Bot Brain"; end-to-end NN control (replaced 300K lines of classical code with single multitask NN); imitation learning from video + Dojo supercomputer; 2.3 kWh, ~2h dynamic runtime
Industrial focus, no fashion aesthetic, no retail integration; Galatea targets Tesla's neural control paradigm applied to fashion domain
The concept of robotic fashion mannequins is no longer theoretical. In October
2025, the Noetix N2 humanoid robot walked down a catwalk at a UNESCO venue in
Paris during Fashion Week, modeling three outfits from a local vintage shop.
TIME Magazine covered it as "Paris Fashion Week's Most Important Model Wasn't
Human." While N2 is child-sized (118cm) with only 18 DOF and struggled with
stairs, its runway appearance validated the core premise: fashion brands and
audiences are ready for robotic mannequins.
Simultaneously, Hans Boodt Mannequins launched AI-sensor-equipped smart
mannequins in 2024 with real-time customer engagement analytics, and 67% of new
motorized mannequin installations in luxury retail now feature programmable
gesture libraries. The market is moving — but no one has built the comprehensive
platform.
Only platform purpose-built for fashion robotics — Every design decision
from actuator selection to AI models is optimized for the fashion retail use
case. No adaptation from industrial/warehouse robotics.
Body morphing — No other humanoid robot can physically adjust its body
proportions. This is a fundamental capability for fashion display, enabling
one robot to model for different sizes and body types.
Fashion-native motion intelligence — Pose libraries, runway walking
styles, and AI motion generation trained specifically on fashion show
footage. Not repurposed industrial motion planning.
Full show engine — Synchronized multi-robot fashion shows with music,
lighting, and formations. No equivalent exists in robotics.
Retail analytics integration — Direct correlation between robot displays
and sales data. POS integration, engagement tracking, A/B testing. Turns
every mannequin into a data-collection point.
Vertical integration — From motor controller firmware to executive
analytics dashboard. Complete platform, not a collection of parts.
Silent operation — < 40 dB target, compared to 55–70 dB for typical
humanoid robots. Essential for retail environments.
Safety-first for public spaces — ISO 13482 compliance built in from the
architecture level, not bolted on. Three-layer safety architecture ensures
safe human-robot coexistence in busy retail environments.
Teleoperation station ops, data pipeline, auto-labeling, dataset curation
Backend Engineers
3–4
Fleet management, analytics, APIs, databases
Frontend Engineers
1–2
Show designer UI, analytics dashboard
Safety/Compliance Engineer
1
ISO certification, risk assessment, testing
Hardware Prototyping
2
PCB design, 3D printing, assembly, testing
Technical Lead
1
Architecture, cross-team coordination
Product Manager
1
Requirements, roadmap, stakeholder management
Total: 35–50 people across the full 42-month development cycle.
Scope Realism Note: The 42-month timeline assumes a well-funded startup or
established robotics division with access to GPU training infrastructure and
experienced roboticists. The stated capabilities represent target
architecture, not Day 1 deliverables. A realistic first product release
(Tier 1 pedestal + Tier 2 wheeled at months 18–24) is achievable with the core
team; Tier 3 bipedal walking and full neural control stack require the full
team and timeline. The proposal is intentionally comprehensive to define the
complete vision — individual phases are independently shippable products.
Competitor firms (Tesla, BD, Figure) have teams of 200–2,000+ people and
billions in funding; Galatea's advantage is narrow domain focus (fashion) vs.
general purpose, not team/budget parity.
The following capabilities are in tension. The proposal acknowledges that not
all can be simultaneously maximized — explicit tradeoff decisions are required
during hardware prototyping:
Tradeoff Pair
Tension
Resolution Strategy
Silent operation vs. torque
Higher torque actuators (gear-driven) are noisier; silent (electromagnetic) have lower torque density
Use electromagnetic for upper body (low torque), QDD/planetary for legs (accept higher noise with acoustic insulation)
Weight vs. battery life
Larger battery extends runtime but increases weight, degrading walking stability and actuator loads
Target 2.4 kWh as the optimal balance; hot-swap design enables continuous operation without oversized battery
To mitigate cost and complexity risks, Galatea supports a tiered deployment
model using the same software stack:
text
Tier 1: STATIC POSING (Lowest cost, lowest risk)
├── Upper-body only on pedestal
├── Pose transitions, face expressions, gestures
├── Body morphing (bust, waist, shoulder)
├── Customer interaction (voice, gaze)
└── Target unit cost: $15,000–25,000
Tier 2: MOBILE BASE (Medium cost, medium risk)
├── Full upper body on omnidirectional wheeled base
├── All Tier 1 features + store navigation
├── Cannot walk bipedally, but can glide smoothly
├── Formation control, show capability
└── Target unit cost: $30,000–50,000
Tier 3: BIPEDAL WALKING (Highest cost, full capability)
├── Full humanoid with bipedal locomotion
├── All Tier 2 features + runway walking
├── Fashion-specific gait styles
├── Full fashion show capability
└── Target unit cost: $60,000–120,000
The global trend in robotics is moving from capital purchase to
Robotics-as-a-Service (RaaS) subscription models. Analysts project the RaaS
market to reach $40 billion by 2030. Galatea's architecture explicitly supports
RaaS deployment from the ground up.
Eliminating disposable mannequin cycling — Traditional mannequins are
replaced every 3–5 years; Galatea robots last 10+ years with maintenance
Reducing sample production — Designers can test garments on morphable
robots instead of producing multiple size samples
Enabling virtual try-on — Customers see garments on their body type
displayed on a robot, reducing return rates (fashion returns account for ~30%
of online purchases)
Digital Product Passport integration — Galatea reads and displays garment
sustainability data (materials, origin, carbon footprint) directly from the EU
DPP, promoting informed purchasing
Safety application processor (Cortex-A class). Pre-certified SIL-3/ASIL-D; deterministic microkernel. Runs on Jetson Thor integrated safety processor or dedicated safety SoC. NOT suitable for MCU-class targets (Cortex-M).
Zephyr RTOS
IEC 61508 SIL-3 (target)
Primary MCU RTOS. Active SIL-3 certification; broad MCU support (STM32H7/Cortex-M7, nRF, ESP32). Runs on joint group controllers and central safety PLC. Open-source, full auditability.
SafeRTOS
IEC 61508 SIL-3 (pre-certified)
Pre-certified MCU RTOS. FreeRTOS derivative; ideal for the most constrained joint-level sub-controllers where Zephyr is too heavy. Pre-certified out of the box.
embOS-Safe
IEC 61508 SIL-3
Backup MCU option. SEGGER's certified RTOS; strong debugger integration; proven in medical devices.
Decision: Architecture-specific RTOS assignment:
Jetson Thor safety processor (Cortex-A): QNX OS for Safety 8.0 — runs
the safety supervisor that monitors neural policy outputs and enforces
force/velocity limits at the application level.
Central safety PLC + joint group controllers (STM32H7 / Cortex-M7):
Zephyr RTOS — runs FOC motor control, encoder read, torque limiting, and
hardware safety functions.
This separation ensures that the MCU-class real-time controllers use an RTOS
appropriate to their architecture, while the application-processor-class
safety supervisor uses QNX's pre-certified microkernel.
Cost-reduced Tier 1/2 option. Sufficient for classical control + lightweight VLA. No LBM inference on-device (offload to cloud).
Custom SoC (future)
TBD
Tesla's approach: purpose-built "Bot Brain" SoC. If Galatea fleet reaches 10K+ units, a custom ASIC with integrated safety cores and neural accelerators becomes cost-effective. Architectural hooks preserved for this path.
Galatea represents a genuinely unprecedented platform — the world's first
comprehensive, full-stack humanoid robotic mannequin system purpose-built for
the fashion industry. No existing robotics company or platform addresses this
use case with the depth and vertical integration that Galatea proposes.
The domain name Galatea (the ivory statue brought to life in Greek mythology)
captures the essential vision with poetic precision: giving life, movement, and
intelligence to what has been, for over a century, a static, lifeless object in
every fashion store on Earth.
By building on the Oshun monorepo's existing capabilities — Aja for motion
intelligence, Sophia for learning, Iris for voice interaction, Euterpe for
music, Yemaya for creative direction — Galatea does not exist in isolation. It
is the physical embodiment of the entire Oshun ecosystem, a robot that walks,
poses, speaks, and performs, powered by every domain working in concert.
Blue ocean, now validated — No competitor builds fashion-specific
humanoid robots. The Noetix N2's Paris Fashion Week debut (October 2025)
proved the concept; Galatea builds the comprehensive platform.
Full vertical integration — Firmware to cloud, motor controller to
analytics dashboard, 148 libraries in one domain.
Body morphing & inclusivity — A capability no humanoid robot possesses
today. One robot, every size, every body type, every gender presentation.
End-to-end neural control — 450M+ parameter Diffusion Transformer with
flow-matching, targeting architectural alignment with Tesla Optimus, Boston
Dynamics Atlas, and Figure AI Helix. Classical control (MPC/PID) serves as
verified safety fallback, not the primary controller.
System 1/System 2 architecture — Industry-standard dual-process control:
DiT at 30Hz + reflex VLA at 200Hz (System 1) + deliberate reasoning at 7–30Hz
(System 2) + Motor Cortex at 1kHz (RT MCU). Targets the same architectural
pattern used by BD, Figure, NVIDIA, and Agility.
Foundation model native — Built on NVIDIA GR00T N1.5 VLA and Jetson Thor
(2,070 TFLOPS). Large Behavior Model trained on 11,600+ hours of mixed data
(teleoperation + simulation + internet video).
Fashion-native AI — HY-Motion DiT, flow matching, and diffusion models
fine-tuned on fashion data. Not repurposed industrial motion planning.
First-class teleoperation & training pipeline — VR teleoperation
stations, sensor suits, auto-labeling, distributed GPU training, TensorRT
deployment — the full data-to-deployment pipeline that top firms use.
Show engine — Synchronized multi-robot fashion shows with music,
lighting, and formations at sub-millisecond precision.
Safety-first architecture — Three-layer safety with ISO 13482 / ISO/FDIS
13482 compliance, QNX OS for Safety 8.0 (SIL-3/ASIL-D), and policy
confidence monitoring with automatic classical fallback.
RaaS-ready — Subscription model from $1,500/mo, with tiered hardware
from $15K pedestals to $120K bipedal walkers. Accessible to independents and
flagships alike.
Federated fleet learning — Every robot improves the fleet. Edge case
flagging, privacy-preserving gradient aggregation, Digital Nervous System
for continuous model improvement across deployed fleet.
Data-driven retail — Every robot is an analytics endpoint, correlating
display decisions with actual sales revenue. A/B testing, heatmaps, POS
integration.
Sustainable by design — LiFePO4 batteries (2,000+ cycles), <500
kWh/year, 95%+ recyclable by weight, EU Digital Product Passport
integration.
Galatea doesn't just replace the mannequin. It makes the mannequin the most
intelligent, data-rich, inclusive, and captivating element in the entire retail
experience.